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train_C-41

Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems

A challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing effective adaptive resource management strategies that can meet end-to-end quality of service (QoS) requirements in varying operational conditions. This paper presents two contributions to research in adaptive resource management for DRE systems. First, we describe the structure and functionality of the Hybrid Adaptive Resourcemanagement Middleware (HyARM), which provides adaptive resource management using hybrid control techniques for adapting to workload fluctuations and resource availability. Second, we evaluate the adaptive behavior of HyARM via experiments on a DRE multimedia system that distributes video in real-time. Our results indicate that HyARM yields predictable, stable, and high system performance, even in the face of fluctuating workload and resource availability.

1. INTRODUCTION Achieving end-to-end real-time quality of service (QoS) is particularly important for open distributed real-time and embedded (DRE) systems that face resource constraints, such as limited computing power and network bandwidth. Overutilization of these system resources can yield unpredictable and unstable behavior, whereas under-utilization can yield excessive system cost. A promising approach to meeting these end-to-end QoS requirements effectively, therefore, is to develop and apply adaptive middleware [10, 15], which is software whose functional and QoS-related properties can be modified either statically or dynamically. Static modifications are carried out to reduce footprint, leverage capabilities that exist in specific platforms, enable functional subsetting, and/or minimize hardware/software infrastructure dependencies. Objectives of dynamic modifications include optimizing system responses to changing environments or requirements, such as changing component interconnections, power-levels, CPU and network bandwidth availability, latency/jitter, and workload. In open DRE systems, adaptive middleware must make such modifications dependably, i.e., while meeting stringent end-to-end QoS requirements, which requires the specification and enforcement of upper and lower bounds on system resource utilization to ensure effective use of system resources. To meet these requirements, we have developed the Hybrid Adaptive Resource-management Middleware (HyARM), which is an open-source1 distributed resource management middleware. HyARM is based on hybrid control theoretic techniques [8], which provide a theoretical framework for designing control of complex system with both continuous and discrete dynamics. In our case study, which involves a distributed real-time video distribution system, the task of adaptive resource management is to control the utilization of the different resources, whose utilizations are described by continuous variables. We achieve this by adapting the resolution of the transmitted video, which is modeled as a continuous variable, and by changing the frame-rate and the compression, which are modeled by discrete actions. We have implemented HyARM atop The ACE ORB (TAO) [13], which is an implementation of the Real-time CORBA specification [12]. Our results show that (1) HyARM ensures effective system resource utilization and (2) end-to-end QoS requirements of higher priority applications are met, even in the face of fluctuations in workload. The remainder of the paper is organized as follows: Section 2 describes the architecture, functionality, and resource utilization model of our DRE multimedia system case study; Section 3 explains the structure and functionality of HyARM; Section 4 evaluates the adaptive behavior of HyARM via experiments on our multimedia system case study; Section 5 compares our research on HyARM with related work; and Section 6 presents concluding remarks. 1 The code and examples for HyARM are available at www. dre.vanderbilt.edu/∼nshankar/HyARM/. Article 7 2. CASE STUDY: DRE MULTIMEDIA SYSTEM This section describes the architecture and QoS requirements of our DRE multimedia system. 2.1 Multimedia System Architecture Wireless Link Wireless Link Wireless Link ` ` ` Physical Link Physical Link Physical Link Base Station End Receiver End Receiver End Receiver` Physical Link End Receiver UAV Camera Video Encoder Camera Video Encoder Camera Video Encoder UAV Camera Video Encoder Camera Video Encoder Camera Video Encoder UAV Camera Video Encoder Camera Video Encoder Camera Video Encoder Figure 1: DRE Multimedia System Architecture The architecture for our DRE multimedia system is shown in Figure 1 and consists of the following entities: (1)Data source (video capture by UAV), where video is captured (related to subject of interest) by camera(s) on each UAV, followed by encoding of raw video using a specific encoding scheme and transmitting the video to the next stage in the pipeline. (2)Data distributor (base station), where the video is processed to remove noise, followed by retransmission of the processed video to the next stage in the pipeline. (3) Sinks (command and control center), where the received video is again processed to remove noise, then decoded and finally rendered to end user via graphical displays. Significant improvements in video encoding/decoding and (de)compression techniques have been made as a result of recent advances in video encoding and compression techniques [14]. Common video compression schemes are MPEG1, MPEG-2, Real Video, and MPEG-4. Each compression scheme is characterized by its resource requirement, e.g., the computational power to (de)compress the video signal and the network bandwidth required to transmit the compressed video signal. Properties of the compressed video, such as resolution and frame-rate determine both the quality and the resource requirements of the video. Our multimedia system case study has the following endto-end real-time QoS requirements: (1) latency, (2) interframe delay (also know as jitter), (3) frame rate, and (4) picture resolution. These QoS requirements can be classified as being either hard or soft. Hard QoS requirements should be met by the underlying system at all times, whereas soft QoS requirements can be missed occasionally.2 For our case study, we treat QoS requirements such as latency and jitter as harder QoS requirements and strive to meet these requirements at all times. In contrast, we treat QoS requirements such as video frame rate and picture resolution as softer QoS requirements and modify these video properties adaptively to handle dynamic changes in resource availabil2 Although hard and soft are often portrayed as two discrete requirement sets, in practice they are usually two ends of a continuum ranging from softer to harder rather than two disjoint points. ity effectively. 2.2 DRE Multimedia System Rresources There are two primary types of resources in our DRE multimedia system: (1) processors that provide computational power available at the UAVs, base stations, and end receivers and (2) network links that provide communication bandwidth between UAVs, base stations, and end receivers. The computing power required by the video capture and encoding tasks depends on dynamic factors, such as speed of the UAV, speed of the subject (if the subject is mobile), and distance between UAV and the subject. The wireless network bandwidth available to transmit video captured by UAVs to base stations also depends on the wireless connectivity between the UAVs and the base station, which in-turn depend on dynamic factors such as the speed of the UAVs and the relative distance between UAVs and base stations. The bandwidth of the link between the base station and the end receiver is limited, but more stable than the bandwidth of the wireless network. Resource requirements and availability of resources are subjected to dynamic changes. Two classes of applications - QoS-enabled and best-effort - use the multimedia system infrastructure described above to transmit video to their respective receivers. QoS-enabled class of applications have higher priority over best-effort class of application. In our study, emergency response applications belong to QoS-enabled and surveillance applications belong to best-effort class. For example, since a stream from an emergency response application is of higher importance than a video stream from a surveillance application, it receives more resources end-to-end. Since resource availability significantly affects QoS, we use current resource utilization as the primary indicator of system performance. We refer to the current level of system resource utilization as the system condition. Based on this definition, we can classify system conditions as being either under, over, or effectively utilized. Under-utilization of system resources occurs when the current resource utilization is lower than the desired lower bound on resource utilization. In this system condition, residual system resources (i.e., network bandwidth and computational power) are available in large amounts after meeting end-to-end QoS requirements of applications. These residual resources can be used to increase the QoS of the applications. For example, residual CPU and network bandwidth can be used to deliver better quality video (e.g., with greater resolution and higher frame rate) to end receivers. Over-utilization of system resources occurs when the current resource utilization is higher than the desired upper bound on resource utilization. This condition can arise from loss of resources - network bandwidth and/or computing power at base station, end receiver or at UAV - or may be due to an increase in resource demands by applications. Over-utilization is generally undesirable since the quality of the received video (such as resolution and frame rate) and timeliness properties (such as latency and jitter) are degraded and may result in an unstable (and thus ineffective) system. Effective resource utilization is the desired system condition since it ensures that end-to-end QoS requirements of the UAV-based multimedia system are met and utilization of both system resources, i.e., network bandwidth and computational power, are within their desired utilization bounds. Article 7 Section 3 describes techniques we applied to achieve effective utilization, even in the face of fluctuating resource availability and/or demand. 3. OVERVIEW OF HYARM This section describes the architecture of the Hybrid Adaptive Resource-management Middleware (HyARM). HyARM ensures efficient and predictable system performance by providing adaptive resource management, including monitoring of system resources and enforcing bounds on application resource utilization. 3.1 HyARM Structure and Functionality Resource Utilization Legend Resource Allocation Application Parameters Figure 2: HyARM Architecture HyARM is composed of three types of entities shown in Figure 2 and described below: Resource monitors observe the overall resource utilization for each type of resource and resource utilization per application. In our multimedia system, there are resource monitors for CPU utilization and network bandwidth. CPU monitors observe the CPU resource utilization of UAVs, base station, and end receivers. Network bandwidth monitors observe the network resource utilization of (1) wireless network link between UAVs and the base station and (2) wired network link between the base station and end receivers. The central controller maintains the system resource utilization below a desired bound by (1) processing periodic updates it receives from resource monitors and (2) modifying the execution of applications accordingly, e.g., by using different execution algorithms or operating the application with increased/decreased QoS. This adaptation process ensures that system resources are utilized efficiently and end-to-end application QoS requirements are met. In our multimedia system, the HyARM controller determines the value of application parameters such as (1) video compression schemes, such as Real Video and MPEG-4, and/or (2) frame rate, and (3) picture resolution. From the perspective of hybrid control theoretic techniques [8], the different video compression schemes and frame rate form the discrete variables of application execution and picture resolution forms the continuous variables. Application adapters modify application execution according to parameters recommended by the controller and ensures that the operation of the application is in accordance with the recommended parameters. In the current mplementation of HyARM, the application adapter modifies the input parameters to the application that affect application QoS and resource utilization - compression scheme, frame rate, and picture resolution. In our future implementations, we plan to use resource reservation mechanisms such as Differentiated Service [7, 3] and Class-based Kernel Resource Management [4] to provision/reserve network and CPU resources. In our multimedia system, the application adapter ensures that the video is encoded at the recommended frame rate and resolution using the specified compression scheme. 3.2 Applying HyARM to the Multimedia System Case Study HyARM is built atop TAO [13], a widely used open-source implementation of Real-time CORBA [12]. HyARM can be applied to ensure efficient, predictable and adaptive resource management of any DRE system where resource availability and requirements are subject to dynamic change. Figure 3 shows the interaction of various parts of the DRE multimedia system developed with HyARM, TAO, and TAO"s A/V Streaming Service. TAO"s A/V Streaming service is an implementation of the CORBA A/V Streaming Service specification. TAO"s A/V Streaming Service is a QoS-enabled video distribution service that can transfer video in real-time to one or more receivers. We use the A/V Streaming Service to transmit the video from the UAVs to the end receivers via the base station. Three entities of Receiver UAV TAO Resource Utilization HyARM Central Controller A/V Streaming Service : Sender MPEG1 MPEG4 Real Video HyARM Resource Monitor A/V Streaming Service : Receiver Compressed Video Compressed Video Application HyARM Application Adapter Remote Object Call Control Inputs Resource Utilization Resource Utilization / Control Inputs Control Inputs Legend Figure 3: Developing the DRE Multimedia System with HyARM HyARM, namely the resource monitors, central controller, and application adapters are built as CORBA servants, so they can be distributed throughout a DRE system. Resource monitors are remote CORBA objects that update the central controller periodically with the current resource utilization. Application adapters are collocated with applications since the two interact closely. As shown in Figure 3, UAVs compress the data using various compression schemes, such as MPEG1, MPEG4, and Real Video, and uses TAO"s A/V streaming service to transmit the video to end receivers. HyARM"s resource monitors continuously observe the system resource utilization and notify the central controller with the current utilization. 3 The interaction between the controller and the resource monitors uses the Observer pattern [5]. When the controller receives resource utilization updates from monitors, it computes the necessary modifications to application(s) parameters and notifies application adapter(s) via a remote operation call. Application adapter(s), that are collocated with the application, modify the input parameters to the application - in our case video encoder - to modify the application resource utilization and QoS. 3 The base station is not included in the figure since it only retransmits the video received from UAVs to end receivers. Article 7 4. PERFORMANCE RESULTS AND ANALYSIS This section first describes the testbed that provides the infrastructure for our DRE multimedia system, which was used to evaluate the performance of HyARM. We then describe our experiments and analyze the results obtained to empirically evaluate how HyARM behaves during underand over-utilization of system resources. 4.1 Overview of the Hardware and Software Testbed Our experiments were performed on the Emulab testbed at University of Utah. The hardware configuration consists of two nodes acting as UAVs, one acting as base station, and one as end receiver. Video from the two UAVs were transmitted to a base station via a LAN configured with the following properties: average packet loss ratio of 0.3 and bandwidth 1 Mbps. The network bandwidth was chosen to be 1 Mbps since each UAV in the DRE multimedia system is allocated 250 Kbps. These parameters were chosen to emulate an unreliable wireless network with limited bandwidth between the UAVs and the base station. From the base station, the video was retransmitted to the end receiver via a reliable wireline link of 10 Mbps bandwidth with no packet loss. The hardware configuration of all the nodes was chosen as follows: 600 MHz Intel Pentium III processor, 256 MB physical memory, 4 Intel EtherExpress Pro 10/100 Mbps Ethernet ports, and 13 GB hard drive. A real-time version of Linux - TimeSys Linux/NET 3.1.214 based on RedHat Linux 9was used as the operating system for all nodes. The following software packages were also used for our experiments: (1) Ffmpeg 0.4.9-pre1, which is an open-source library (http: //www.ffmpeg.sourceforge.net/download.php) that compresses video into MPEG-2, MPEG-4, Real Video, and many other video formats. (2) Iftop 0.16, which is an opensource library (http://www.ex-parrot.com/∼pdw/iftop/) we used for monitoring network activity and bandwidth utilization. (3) ACE 5.4.3 + TAO 1.4.3, which is an opensource (http://www.dre.vanderbilt.edu/TAO) implementation of the Real-time CORBA [12] specification upon which HyARM is built. TAO provides the CORBA Audio/Video (A/V) Streaming Service that we use to transmit the video from the UAVs to end receivers via the base station. 4.2 Experiment Configuration Our experiment consisted of two (emulated) UAVs that simultaneously send video to the base station using the experimentation setup described in Section 4.1. At the base station, video was retransmitted to the end receivers (without any modifications), where it was stored to a file. Each UAV hosted two applications, one QoS-enabled application (emergency response), and one best-effort application (surveillance). Within each UAV, computational power is shared between the applications, while the network bandwidth is shared among all applications. To evaluate the QoS provided by HyARM, we monitored CPU utilization at the two UAVs, and network bandwidth utilization between the UAV and the base station. CPU resource utilization was not monitored at the base station and the end receiver since they performed no computationallyintensive operations. The resource utilization of the 10 Mpbs physical link between the base station and the end receiver does not affect QoS of applications and is not monitored by HyARM since it is nearly 10 times the 1 MB bandwidth of the LAN between the UAVs and the base station. The experiment also monitors properties of the video that affect the QoS of the applications, such as latency, jitter, frame rate, and resolution. The set point on resource utilization for each resource was specified at 0.69, which is the upper bound typically recommended by scheduling techniques, such as rate monotonic algorithm [9]. Since studies [6] have shown that human eyes can perceive delays more than 200ms, we use this as the upper bound on jitter of the received video. QoS requirements for each class of application is specified during system initialization and is shown in Table 1. 4.3 Empirical Results and Analysis This section presents the results obtained from running the experiment described in Section 4.2 on our DRE multimedia system testbed. We used system resource utilization as a metric to evaluate the adaptive resource management capabilities of HyARM under varying input work loads. We also used application QoS as a metric to evaluate HyARM"s capabilities to support end-to-end QoS requirements of the various classes of applications in the DRE multimedia system. We analyze these results to explain the significant differences in system performance and application QoS. Comparison of system performance is decomposed into comparison of resource utilization and application QoS. For system resource utilization, we compare (1) network bandwidth utilization of the local area network and (2) CPU utilization at the two UAV nodes. For application QoS, we compare mean values of video parameters, including (1) picture resolution, (2) frame rate, (3) latency, and (4) jitter. Comparison of resource utilization. Over-utilization of system resources in DRE systems can yield an unstable system. In contrast, under-utilization of system resources increases system cost. Figure 4 and Figure 5 compare the system resource utilization with and without HyARM. Figure 4 shows that HyARM maintains system utilization close to the desired utilization set point during fluctuation in input work load by transmitting video of higher (or lower) QoS for QoS-enabled (or best-effort) class of applications during over (or under) utilization of system resources. Figure 5 shows that without HyARM, network utilization was as high as 0.9 during increase in workload conditions, which is greater than the utilization set point of 0.7 by 0.2. As a result of over-utilization of resources, QoS of the received video, such as average latency and jitter, was affected significantly. Without HyARM, system resources were either under-utilized or over-utilized, both of which are undesirable. In contrast, with HyARM, system resource utilization is always close to the desired set point, even during fluctuations in application workload. During sudden fluctuation in application workload, system conditions may be temporarily undesirable, but are restored to the desired condition within several sampling periods. Temporary over-utilization of resources is permissible in our multimedia system since the quality of the video may be degraded for a short period of time, though application QoS will be degraded significantly if poor quality video is transmitted for a longer period of time. Comparison of application QoS. Figures 6, Figure 7, and Table 2 compare latency, jitter, resolution, and frameArticle 7 Class Resolution Frame Rate Latency (msec ) Jitter (msec) QoS Enabled 1024 x 768 25 200 200 Best-effort 320 x 240 15 300 250 Table 1: Application QoS Requirements Figure 4: Resource utilization with HyARM Figure 5: Resource utilization without HyARM rate of the received video, respectively. Table 2 shows that HyARM increases the resolution and frame video of QoSenabled applications, but decreases the resolution and frame rate of best effort applications. During over utilization of system resources, resolution and frame rate of lower priority applications are reduced to adapt to fluctuations in application workload and to maintain the utilization of resources at the specified set point. It can be seen from Figure 6 and Figure 7 that HyARM reduces the latency and jitter of the received video significantly. These figures show that the QoS of QoS-enabled applications is greatly improved by HyARM. Although application parameters, such as frame rate and resolutions, which affect the soft QoS requirements of best-effort applications may be compromised, the hard QoS requirements, such as latency and jitter, of all applications are met. HyARM responds to fluctuation in resource availability and/or demand by constant monitoring of resource utilization. As shown in Figure 4, when resources utilization increases above the desired set point, HyARM lowers the utilization by reducing the QoS of best-effort applications. This adaptation ensures that enough resources are available for QoS-enabled applications to meet their QoS needs. Figures 6 and 7 show that the values of latency and jitter of the received video of the system with HyARM are nearly half of the corresponding value of the system without HyARM. With HyARM, values of these parameters are well below the specified bounds, whereas without HyARM, these value are significantly above the specified bounds due to overutilization of the network bandwidth, which leads to network congestion and results in packet loss. HyARM avoids this by reducing video parameters such as resolution, frame-rate, and/or modifying the compression scheme used to compress the video. Our conclusions from analyzing the results described above are that applying adaptive middleware via hybrid control to DRE system helps to (1) improve application QoS, (2) increase system resource utilization, and (3) provide better predictability (lower latency and inter-frame delay) to QoSenabled applications. These improvements are achieved largely due to monitoring of system resource utilization, efficient system workload management, and adaptive resource provisioning by means of HyARM"s network/CPU resource monitors, application adapter, and central controller, respectively. 5. RELATED WORK A number of control theoretic approaches have been applied to DRE systems recently. These techniques aid in overcoming limitations with traditional scheduling approaches that handle dynamic changes in resource availability poorly and result in a rigidly scheduled system that adapts poorly to change. A survey of these techniques is presented in [1]. One such approach is feedback control scheduling (FCS) [2, 11]. FCS algorithms dynamically adjust resource allocation by means of software feedback control loops. FCS algorithms are modeled and designed using rigorous controltheoretic methodologies. These algorithms provide robust and analytical performance assurances despite uncertainties in resource availability and/or demand. Although existing FCS algorithms have shown promise, these algorithms often assume that the system has continuous control variable(s) that can continuously be adjusted. While this assumption holds for certain classes of systems, there are many classes of DRE systems, such as avionics and total-ship computing environments that only support a finite a priori set of discrete configurations. The control variables in such systems are therefore intrinsically discrete. HyARM handles both continuous control variables, such as picture resolution, and discrete control variable, such as discrete set of frame rates. HyARM can therefore be applied to system that support continuous and/or discrete set of control variables. The DRE multimedia system as described in Section 2 is an example DRE system that offers both continuous (picture resolution) and discrete set (frame-rate) of control variables. These variables are modified by HyARM to achieve efficient resource utilization and improved application QoS. 6. CONCLUDING REMARKS Article 7 Figure 6: Comparison of Video Latency Figure 7: Comparison of Video Jitter Source Picture Size / Frame Rate With HyARM Without HyARM UAV1 QoS Enabled Application 1122 X 1496 / 25 960 X 720 / 20 UAV1 Best-effort Application 288 X 384 / 15 640 X 480 / 20 UAV2 QoS Enabled Application 1126 X 1496 / 25 960 X 720 / 20 UAV2 Best-effort Application 288 X 384 / 15 640 X 480 / 20 Table 2: Comparison of Video Quality Many distributed real-time and embedded (DRE) systems demand end-to-end quality of service (QoS) enforcement from their underlying platforms to operate correctly. These systems increasingly run in open environments, where resource availability is subject to dynamic change. To meet end-to-end QoS in dynamic environments, DRE systems can benefit from an adaptive middleware that monitors system resources, performs efficient application workload management, and enables efficient resource provisioning for executing applications. This paper described HyARM, an adaptive middleware, that provides effective resource management to DRE systems. HyARM employs hybrid control techniques to provide the adaptive middleware capabilities, such as resource monitoring and application adaptation that are key to providing the dynamic resource management capabilities for open DRE systems. We employed HyARM to a representative DRE multimedia system that is implemented using Real-time CORBA and CORBA A/V Streaming Service. We evaluated the performance of HyARM in a system composed of three distributed resources and two classes of applications with two applications each. Our empirical results indicate that HyARM ensures (1) efficient resource utilization by maintaining the resource utilization of system resources within the specified utilization bounds, (2) QoS requirements of QoS-enabled applications are met at all times. Overall, HyARM ensures efficient, predictable, and adaptive resource management for DRE systems. 7. REFERENCES [1] T. F. Abdelzaher, J. Stankovic, C. Lu, R. Zhang, and Y. Lu. Feddback Performance Control in Software Services. IEEE: Control Systems, 23(3), June 2003. [2] L. Abeni, L. Palopoli, G. Lipari, and J. Walpole. Analysis of a reservation-based feedback scheduler. In IEEE Real-Time Systems Symposium, Dec. 2002. [3] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss. An architecture for differentiated services. Network Information Center RFC 2475, Dec. 1998. [4] H. Franke, S. Nagar, C. Seetharaman, and V. Kashyap. Enabling Autonomic Workload Management in Linux. In Proceedings of the International Conference on Autonomic Computing (ICAC), New York, New York, May 2004. IEEE. [5] E. Gamma, R. Helm, R. Johnson, and J. Vlissides. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley, Reading, MA, 1995. [6] G. Ghinea and J. P. Thomas. Qos impact on user perception and understanding of multimedia video clips. In MULTIMEDIA "98: Proceedings of the sixth ACM international conference on Multimedia, pages 49-54, Bristol, United Kingdom, 1998. ACM Press. [7] Internet Engineering Task Force. Differentiated Services Working Group (diffserv) Charter. www.ietf.org/html.charters/diffserv-charter.html, 2000. [8] X. Koutsoukos, R. Tekumalla, B. Natarajan, and C. Lu. Hybrid Supervisory Control of Real-Time Systems. In 11th IEEE Real-Time and Embedded Technology and Applications Symposium, San Francisco, California, Mar. 2005. [9] J. Lehoczky, L. Sha, and Y. Ding. The Rate Monotonic Scheduling Algorithm: Exact Characterization and Average Case Behavior. In Proceedings of the 10th IEEE Real-Time Systems Symposium (RTSS 1989), pages 166-171. IEEE Computer Society Press, 1989. [10] J. Loyall, J. Gossett, C. Gill, R. Schantz, J. Zinky, P. Pal, R. Shapiro, C. Rodrigues, M. Atighetchi, and D. Karr. Comparing and Contrasting Adaptive Middleware Support in Wide-Area and Embedded Distributed Object Applications. In Proceedings of the 21st International Conference on Distributed Computing Systems (ICDCS-21), pages 625-634. IEEE, Apr. 2001. [11] C. Lu, J. A. Stankovic, G. Tao, and S. H. Son. Feedback Control Real-Time Scheduling: Framework, Modeling, and Algorithms. Real-Time Systems Journal, 23(1/2):85-126, July 2002. [12] Object Management Group. Real-time CORBA Specification, OMG Document formal/02-08-02 edition, Aug. 2002. [13] D. C. Schmidt, D. L. Levine, and S. Mungee. The Design and Performance of Real-Time Object Request Brokers. Computer Communications, 21(4):294-324, Apr. 1998. [14] Thomas Sikora. Trends and Perspectives in Image and Video Coding. In Proceedings of the IEEE, Jan. 2005. [15] X. Wang, H.-M. Huang, V. Subramonian, C. Lu, and C. Gill. CAMRIT: Control-based Adaptive Middleware for Real-time Image Transmission. In Proc. of the 10th IEEE Real-Time and Embedded Tech. and Applications Symp. (RTAS), Toronto, Canada, May 2004. Article 7

real-time video distribution system;dynamic environment;hybrid system;video encoding/decoding;quality of service;streaming service;hybrid adaptive resourcemanagement middleware;distributed real-time embedded system;distribute real-time embed system;adaptive resource management;service end-to-end quality;hybrid control technique;service quality;real-time corba specification;end-to-end quality of service;resource reservation mechanism

train_C-42

Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization

Ensemble Kalman filter data assimilation methodology is a popular approach for hydrocarbon reservoir simulations in energy exploration. In this approach, an ensemble of geological models and production data of oil fields is used to forecast the dynamic response of oil wells. The Schlumberger ECLIPSE software is used for these simulations. Since models in the ensemble do not communicate, message-passing implementation is a good choice. Each model checks out an ECLIPSE license and therefore, parallelizability of reservoir simulations depends on the number licenses available. We have Grid-enabled the ensemble Kalman filter data assimilation methodology for the TIGRE Grid computing environment. By pooling the licenses and computing resources across the collaborating institutions using GridWay metascheduler and TIGRE environment, the computational accuracy can be increased while reducing the simulation runtime. In this paper, we provide an account of our efforts in Gridenabling the ensemble Kalman Filter data assimilation methodology. Potential benefits of this approach, observations and lessons learned will be discussed.

1. INTRODUCTION Grid computing [1] is an emerging collaborative computing paradigm to extend institution/organization specific high performance computing (HPC) capabilities greatly beyond local resources. Its importance stems from the fact that ground breaking research in strategic application areas such as bioscience and medicine, energy exploration and environmental modeling involve strong interdisciplinary components and often require intercampus collaborations and computational capabilities beyond institutional limitations. The Texas Internet Grid for Research and Education (TIGRE) [2,3] is a state funded cyberinfrastructure development project carried out by five (Rice, A&M, TTU, UH and UT Austin) major university systems - collectively called TIGRE Institutions. The purpose of TIGRE is to create a higher education Grid to sustain and extend research and educational opportunities across Texas. TIGRE is a project of the High Performance Computing across Texas (HiPCAT) [4] consortium. The goal of HiPCAT is to support advanced computational technologies to enhance research, development, and educational activities. The primary goal of TIGRE is to design and deploy state-of-the-art Grid middleware that enables integration of computing systems, storage systems and databases, visualization laboratories and displays, and even instruments and sensors across Texas. The secondary goal is to demonstrate the TIGRE capabilities to enhance research and educational opportunities in strategic application areas of interest to the State of Texas. These are bioscience and medicine, energy exploration and air quality modeling. Vision of the TIGRE project is to foster interdisciplinary and intercampus collaborations, identify novel approaches to extend academic-government-private partnerships, and become a competitive model for external funding opportunities. The overall goal of TIGRE is to support local, campus and regional user interests and offer avenues to connect with national Grid projects such as Open Science Grid [5], and TeraGrid [6]. Within the energy exploration strategic application area, we have Grid-enabled the ensemble Kalman Filter (EnKF) [7] approach for data assimilation in reservoir modeling and demonstrated the extensibility of the application using the TIGRE environment and the GridWay [8] metascheduler. Section 2 provides an overview of the TIGRE environment and capabilities. Application description and the need for Grid-enabling EnKF methodology is provided in Section 3. The implementation details and merits of our approach are discussed in Section 4. Conclusions are provided in Section 5. Finally, observations and lessons learned are documented in Section 6. 2. TIGRE ENVIRONMENT The TIGRE Grid middleware consists of minimal set of components derived from a subset of the Virtual Data Toolkit (VDT) [9] which supports a variety of operating systems. The purpose of choosing a minimal software stack is to support applications at hand, and to simplify installation and distribution of client/server stacks across TIGRE sites. Additional components will be added as they become necessary. The PacMan [10] packaging and distribution mechanism is employed for TIGRE client/server installation and management. The PacMan distribution mechanism involves retrieval, installation, and often configuration of the packaged software. This approach allows the clients to keep current, consistent versions of TIGRE software. It also helps TIGRE sites to install the needed components on resources distributed throughout the participating sites. The TIGRE client/server stack consists of an authentication and authorization layer, Globus GRAM4-based job submission via web services (pre-web services installations are available up on request). The tools for handling Grid proxy generation, Grid-enabled file transfer and Grid-enabled remote login are supported. The pertinent details of TIGRE services and tools for job scheduling and management are provided below. 2.1. Certificate Authority The TIGRE security infrastructure includes a certificate authority (CA) accredited by the International Grid Trust Federation (IGTF) for issuing X. 509 user and resource Grid certificates [11]. The Texas Advanced Computing Center (TACC), University of Texas at Austin is the TIGRE"s shared CA. The TIGRE Institutions serve as Registration Authorities (RA) for their respective local user base. For up-to-date information on securing user and resource certificates and their installation instructions see ref [2]. The users and hosts on TIGRE are identified by their distinguished name (DN) in their X.509 certificate provided by the CA. A native Grid-mapfile that contains a list of authorized DNs is used to authenticate and authorize user job scheduling and management on TIGRE site resources. At Texas Tech University, the users are dynamically allocated one of the many generic pool accounts. This is accomplished through the Grid User Management System (GUMS) [12]. 2.2. Job Scheduling and Management The TIGRE environment supports GRAM4-based job submission via web services. The job submission scripts are generated using XML. The web services GRAM translates the XML scripts into target cluster specific batch schedulers such as LSF, PBS, or SGE. The high bandwidth file transfer protocols such as GridFTP are utilized for staging files in and out of the target machine. The login to remote hosts for compilation and debugging is only through GSISSH service which requires resource authentication through X.509 certificates. The authentication and authorization of Grid jobs are managed by issuing Grid certificates to both users and hosts. The certificate revocation lists (CRL) are updated on a daily basis to maintain high security standards of the TIGRE Grid services. The TIGRE portal [2] documentation area provides a quick start tutorial on running jobs on TIGRE. 2.3. Metascheduler The metascheduler interoperates with the cluster level batch schedulers (such as LSF, PBS) in the overall Grid workflow management. In the present work, we have employed GridWay [8] metascheduler - a Globus incubator project - to schedule and manage jobs across TIGRE. The GridWay is a light-weight metascheduler that fully utilizes Globus functionalities. It is designed to provide efficient use of dynamic Grid resources by multiple users for Grid infrastructures built on top of Globus services. The TIGRE site administrator can control the resource sharing through a powerful built-in scheduler provided by GridWay or by extending GridWay"s external scheduling module to provide their own scheduling policies. Application users can write job descriptions using GridWay"s simple and direct job template format (see Section 4 for details) or standard Job Submission Description Language (JSDL). See section 4 for implementation details. 2.4. Customer Service Management System A TIGRE portal [2] was designed and deployed to interface users and resource providers. It was designed using GridPort [13] and is maintained by TACC. The TIGRE environment is supported by open source tools such as the Open Ticket Request System (OTRS) [14] for servicing trouble tickets, and MoinMoin [15] Wiki for TIGRE content and knowledge management for education, outreach and training. The links for OTRS and Wiki are consumed by the TIGRE portal [2] - the gateway for users and resource providers. The TIGRE resource status and loads are monitored by the Grid Port Information Repository (GPIR) service of the GridPort toolkit [13] which interfaces with local cluster load monitoring service such as Ganglia. The GPIR utilizes cron jobs on each resource to gather site specific resource characteristics such as jobs that are running, queued and waiting for resource allocation. 3. ENSEMBLE KALMAN FILTER APPLICATION The main goal of hydrocarbon reservoir simulations is to forecast the production behavior of oil and gas field (denoted as field hereafter) for its development and optimal management. In reservoir modeling, the field is divided into several geological models as shown in Figure 1. For accurate performance forecasting of the field, it is necessary to reconcile several geological models to the dynamic response of the field through history matching [16-20]. Figure 1. Cross-sectional view of the Field. Vertical layers correspond to different geological models and the nails are oil wells whose historical information will be used for forecasting the production behavior. (Figure Ref:http://faculty.smu.edu/zchen/research.html). The EnKF is a Monte Carlo method that works with an ensemble of reservoir models. This method utilizes crosscovariances [21] between the field measurements and the reservoir model parameters (derived from several models) to estimate prediction uncertainties. The geological model parameters in the ensemble are sequentially updated with a goal to minimize the prediction uncertainties. Historical production response of the field for over 50 years is used in these simulations. The main advantage of EnKF is that it can be readily linked to any reservoir simulator, and can assimilate latest production data without the need to re-run the simulator from initial conditions. Researchers in Texas are large subscribers of the Schlumberger ECLIPSE [22] package for reservoir simulations. In the reservoir modeling, each geological model checks out an ECLIPSE license. The simulation runtime of the EnKF methodology depends on the number of geological models used, number of ECLIPSE licenses available, production history of the field, and propagated uncertainties in history matching. The overall EnKF workflow is shown Figure 2. Figure 2. Ensemble Kaman Filter Data Assimilation Workflow. Each site has L licenses. At START, the master/control process (EnKF main program) reads the simulation configuration file for number (N) of models, and model-specific input files. Then, N working directories are created to store the output files. At the end of iteration, the master/control process collects the output files from N models and post processes crosscovariances [21] to estimate the prediction uncertainties. This information will be used to update models (or input files) for the next iteration. The simulation continues until the production histories are exhausted. Typical EnKF simulation with N=50 and field histories of 50-60 years, in time steps ranging from three months to a year, takes about three weeks on a serial computing environment. In parallel computing environment, there is no interprocess communication between the geological models in the ensemble. However, at the end of each simulation time-step, model-specific output files are to be collected for analyzing cross covariances [21] and to prepare next set of input files. Therefore, master-slave model in messagepassing (MPI) environment is a suitable paradigm. In this approach, the geological models are treated as slaves and are distributed across the available processors. The master Cluster or (TIGRE/GridWay) START Read Configuration File Create N Working Directories Create N Input files Model l Model 2 Model N. . . ECLIPSE on site A ECLIPSE on Site B ECLIPSE on Site Z Collect N Model Outputs, Post-process Output files END . . . process collects model-specific output files, analyzes and prepares next set of input files for the simulation. Since each geological model checks out an ECLIPSE license, parallelizability of the simulation depends on the number of licenses available. When the available number of licenses is less than the number of models in the ensemble, one or more of the nodes in the MPI group have to handle more than one model in a serial fashion and therefore, it takes longer to complete the simulation. A Petroleum Engineering Department usually procures 10-15 ECLIPSE licenses while at least ten-fold increase in the number of licenses would be necessary for industry standard simulations. The number of licenses can be increased by involving several Petroleum Engineering Departments that support ECLIPSE package. Since MPI does not scale very well for applications that involve remote compute clusters, and to get around the firewall issues with license servers across administrative domains, Grid-enabling the EnKF workflow seems to be necessary. With this motivation, we have implemented Grid-enabled EnKF workflow for the TIGRE environment and demonstrated parallelizability of the application across TIGRE using GridWay metascheduler. Further details are provided in the next section. 4. IMPLEMENTATION DETAILS To Grid-enable the EnKF approach, we have eliminated the MPI code for parallel processing and replaced with N single processor jobs (or sub-jobs) where, N is the number of geological models in the ensemble. These model-specific sub-jobs were distributed across TIGRE sites that support ECLIPSE package using the GridWay [8] metascheduler. For each sub-job, we have constructed a GridWay job template that specifies the executable, input and output files, and resource requirements. Since the TIGRE compute resources are not expected to change frequently, we have used static resource discovery policy for GridWay and the sub-jobs were scheduled dynamically across the TIGRE resources using GridWay. Figure 3 represents the sub-job template file for the GridWay metascheduler. Figure 3. GridWay Sub-Job Template In Figure 3, REQUIREMENTS flag is set to choose the resources that satisfy the application requirements. In the case of EnKF application, for example, we need resources that support ECLIPSE package. ARGUMENTS flag specifies the model in the ensemble that will invoke ECLIPSE at a remote site. INPUT_FILES is prepared by the EnKF main program (or master/control process) and is transferred by GridWay to the remote site where it is untared and is prepared for execution. Finally, OUTPUT_FILES specifies the name and location where the output files are to be written. The command-line features of GridWay were used to collect and process the model-specific outputs to prepare new set of input files. This step mimics MPI process synchronization in master-slave model. At the end of each iteration, the compute resources and licenses are committed back to the pool. Table 1 shows the sub-jobs in TIGRE Grid via GridWay using gwps command and for clarity, only selected columns were shown . USER JID DM EM NAME HOST pingluo 88 wrap pend enkf.jt antaeus.hpcc.ttu.edu/LSF pingluo 89 wrap pend enkf.jt antaeus.hpcc.ttu.edu/LSF pingluo 90 wrap actv enkf.jt minigar.hpcc.ttu.edu/LSF pingluo 91 wrap pend enkf.jt minigar.hpcc.ttu.edu/LSF pingluo 92 wrap done enkf.jt cosmos.tamu.edu/PBS pingluo 93 wrap epil enkf.jt cosmos.tamu.edu/PBS Table 1. Job scheduling across TIGRE using GridWay Metascheduler. DM: Dispatch state, EM: Execution state, JID is the job id and HOST corresponds to site specific cluster and its local batch scheduler. When a job is submitted to GridWay, it will go through a series of dispatch (DM) and execution (EM) states. For DM, the states include pend(ing), prol(og), wrap(per), epil(og), and done. DM=prol means the job has been scheduled to a resource and the remote working directory is in preparation. DM=warp implies that GridWay is executing the wrapper which in turn executes the application. DM=epil implies the job has finished running at the remote site and results are being transferred back to the GridWay server. Similarly, when EM=pend implies the job is waiting in the queue for resource and the job is running when EM=actv. For complete list of message flags and their descriptions, see the documentation in ref [8]. We have demonstrated the Grid-enabled EnKF runs using GridWay for TIGRE environment. The jobs are so chosen that the runtime doesn"t exceed more than a half hour. The simulation runs involved up to 20 jobs between A&M and TTU sites with TTU serving 10 licenses. For resource information, see Table I. One of the main advantages of Grid-enabled EnKF simulation is that both the resources and licenses are released back to the pool at the end of each simulation time step unlike in the case of MPI implementation where licenses and nodes are locked until the completion of entire simulation. However, the fact that each sub-job gets scheduled independently via GridWay could possibly incur another time delay caused by waiting in queue for execution in each simulation time step. Such delays are not expected EXECUTABLE=runFORWARD REQUIREMENTS=HOSTNAME=cosmos.tamu.edu | HOSTNAME=antaeus.hpcc.ttu.edu | HOSTNAME=minigar.hpcc.ttu.edu | ARGUMENTS=001 INPUT_FILES=001.in.tar OUTPUT_FILES=001.out.tar in MPI implementation where the node is blocked for processing sub-jobs (model-specific calculation) until the end of the simulation. There are two main scenarios for comparing Grid and cluster computing approaches. Scenario I: The cluster is heavily loaded. The conceived average waiting time of job requesting large number of CPUs is usually longer than waiting time of jobs requesting single CPU. Therefore, overall waiting time could be shorter in Grid approach which requests single CPU for each sub-job many times compared to MPI implementation that requests large number of CPUs at a single time. It is apparent that Grid scheduling is beneficial especially when cluster is heavily loaded and requested number of CPUs for the MPI job is not readily available. Scenario II: The cluster is relatively less loaded or largely available. It appears the MPI implementation is favorable compared to the Grid scheduling. However, parallelizability of the EnKF application depends on the number of ECLIPSE licenses and ideally, the number of licenses should be equal to the number of models in the ensemble. Therefore, if a single institution does not have sufficient number of licenses, the cluster availability doesn"t help as much as it is expected. Since the collaborative environment such as TIGRE can address both compute and software resource requirements for the EnKF application, Grid-enabled approach is still advantageous over the conventional MPI implementation in any of the above scenarios. 5. CONCLUSIONS AND FUTURE WORK TIGRE is a higher education Grid development project and its purpose is to sustain and extend research and educational opportunities across Texas. Within the energy exploration application area, we have Grid-enabled the MPI implementation of the ensemble Kalman filter data assimilation methodology for reservoir characterization. This task was accomplished by removing MPI code for parallel processing and replacing with single processor jobs one for each geological model in the ensemble. These single processor jobs were scheduled across TIGRE via GridWay metascheduler. We have demonstrated that by pooling licenses across TIGRE sites, more geological models can be handled in parallel and therefore conceivably better simulation accuracy. This approach has several advantages over MPI implementation especially when a site specific cluster is heavily loaded and/or the number licenses required for the simulation is more than those available at a single site. Towards the future work, it would be interesting to compare the runtime between MPI, and Grid implementations for the EnKF application. This effort could shed light on quality of service (QoS) of Grid environments in comparison with cluster computing. Another aspect of interest in the near future would be managing both compute and license resources to address the job (or processor)-to-license ratio management. 6. OBSERVATIONS AND LESSIONS LEARNED The Grid-enabling efforts for EnKF application have provided ample opportunities to gather insights on the visibility and promise of Grid computing environments for application development and support. The main issues are industry standard data security and QoS comparable to cluster computing. Since the reservoir modeling research involves proprietary data of the field, we had to invest substantial efforts initially in educating the application researchers on the ability of Grid services in supporting the industry standard data security through role- and privilege-based access using X.509 standard. With respect to QoS, application researchers expect cluster level QoS with Grid environments. Also, there is a steep learning curve in Grid computing compared to the conventional cluster computing. Since Grid computing is still an emerging technology, and it spans over several administrative domains, Grid computing is still premature especially in terms of the level of QoS although, it offers better data security standards compared to commodity clusters. It is our observation that training and outreach programs that compare and contrast the Grid and cluster computing environments would be a suitable approach for enhancing user participation in Grid computing. This approach also helps users to match their applications and abilities Grids can offer. In summary, our efforts through TIGRE in Grid-enabling the EnKF data assimilation methodology showed substantial promise in engaging Petroleum Engineering researchers through intercampus collaborations. Efforts are under way to involve more schools in this effort. These efforts may result in increased collaborative research, educational opportunities, and workforce development through graduate/faculty research programs across TIGRE Institutions. 7. ACKNOWLEDGMENTS The authors acknowledge the State of Texas for supporting the TIGRE project through the Texas Enterprise Fund, and TIGRE Institutions for providing the mechanism, in which the authors (Ravi Vadapalli, Taesung Kim, and Ping Luo) are also participating. The authors thank the application researchers Prof. Akhil Datta-Gupta of Texas A&M University and Prof. Lloyd Heinze of Texas Tech University for their discussions and interest to exploit the TIGRE environment to extend opportunities in research and development. 8. REFERENCES [1] Foster, I. and Kesselman, C. (eds.) 2004. The Grid: Blueprint for a new computing infrastructure (The Elsevier series in Grid computing) [2] TIGRE Portal: http://tigreportal.hipcat.net [3] Vadapalli, R. Sill, A., Dooley, R., Murray, M., Luo, P., Kim, T., Huang, M., Thyagaraja, K., and Chaffin, D. 2007. Demonstration of TIGRE environment for Grid enabled/suitable applications. 8th IEEE/ACM Int. Conf. on Grid Computing, Sept 19-21, Austin [4] The High Performance Computing across Texas Consortium http://www.hipcat.net [5] Pordes, R. Petravick, D. Kramer, B. Olson, D. Livny, M. Roy, A. Avery, P. Blackburn, K. Wenaus, T. Würthwein, F. Foster, I. Gardner, R. Wilde, M. Blatecky, A. McGee, J. and Quick, R. 2007. The Open Science Grid, J. Phys Conf Series http://www.iop.org/EJ/abstract/1742-6596/78/1/012057 and http://www.opensciencegrid.org [6] Reed, D.A. 2003. Grids, the TeraGrid and Beyond, Computer, vol 30, no. 1 and http://www.teragrid.org [7] Evensen, G. 2006. Data Assimilation: The Ensemble Kalman Filter, Springer [8] Herrera, J. Huedo, E. Montero, R. S. and Llorente, I. M. 2005. Scientific Programming, vol 12, No. 4. pp 317-331 [9] Avery, P. and Foster, I. 2001. The GriPhyN project: Towards petascale virtual data grids, technical report GriPhyN-200115 and http://vdt.cs.wisc.edu [10] The PacMan documentation and installation guide http://physics.bu.edu/pacman/htmls [11] Caskey, P. Murray, M. Perez, J. and Sill, A. 2007. Case studies in identify management for virtual organizations, EDUCAUSE Southwest Reg. Conf., Feb 21-23, Austin, TX. http://www.educause.edu/ir/library/pdf/SWR07058.pdf [12] The Grid User Management System (GUMS) https://www.racf.bnl.gov/Facility/GUMS/index.html [13] Thomas, M. and Boisseau, J. 2003. Building grid computing portals: The NPACI grid portal toolkit, Grid computing: making the global infrastructure a reality, Chapter 28, Berman, F. Fox, G. Thomas, M. Boisseau, J. and Hey, T. (eds), John Wiley and Sons, Ltd, Chichester [14] Open Ticket Request System http://otrs.org [15] The MoinMoin Wiki Engine http://moinmoin.wikiwikiweb.de [16] Vasco, D.W. Yoon, S. and Datta-Gupta, A. 1999. Integrating dynamic data into high resolution reservoir models using streamline-based analytic sensitivity coefficients, Society of Petroleum Engineers (SPE) Journal, 4 (4). [17] Emanuel, A. S. and Milliken, W. J. 1998. History matching finite difference models with 3D streamlines, SPE 49000, Proc of the Annual Technical Conf and Exhibition, Sept 2730, New Orleans, LA. [18] Nævdal, G. Johnsen, L.M. Aanonsen, S.I. and Vefring, E.H. 2003. Reservoir monitoring and Continuous Model Updating using Ensemble Kalman Filter, SPE 84372, Proc of the Annual Technical Conf and Exhibition, Oct 5-8, Denver, CO. [19] Jafarpour B. and McLaughlin, D.B. 2007. History matching with an ensemble Kalman filter and discrete cosine parameterization, SPE 108761, Proc of the Annual Technical Conf and Exhibition, Nov 11-14, Anaheim, CA [20] Li, G. and Reynolds, A. C. 2007. An iterative ensemble Kalman filter for data assimilation, SPE 109808, Proc of the SPE Annual Technical Conf and Exhibition, Nov 11-14, Anaheim, CA [21] Arroyo-Negrete, E. Devagowda, D. Datta-Gupta, A. 2006. Streamline assisted ensemble Kalman filter for rapid and continuous reservoir model updating. Proc of the Int. Oil & Gas Conf and Exhibition, SPE 104255, Dec 5-7, China [22] ECLIPSE Reservoir Engineering Software http://www.slb.com/content/services/software/reseng/index.a sp

pooling license;grid-enabling;ensemble kalman filter;and gridway;cyberinfrastructure development project;tigre grid computing environment;grid computing;hydrocarbon reservoir simulation;gridway metascheduler;enkf;datum assimilation methodology;high performance computing;tigre;energy exploration;tigre grid middleware;strategic application area;reservoir model

train_C-44

MSP: Multi-Sequence Positioning of Wireless Sensor Nodes∗

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations, which work together to increase the localization accuracy. We address several interesting issues, such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). This evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. It also provides a nice tradeoff between physical cost (anchors) and soft cost (events), while maintaining localization accuracy.

1 Introduction Although Wireless Sensor Networks (WSN) have shown promising prospects in various applications [5], researchers still face several challenges for massive deployment of such networks. One of these is to identify the location of individual sensor nodes in outdoor environments. Because of unpredictable flow dynamics in airborne scenarios, it is not currently feasible to localize sensor nodes during massive UVA-based deployment. On the other hand, geometric information is indispensable in these networks, since users need to know where events of interest occur (e.g., the location of intruders or of a bomb explosion). Previous research on node localization falls into two categories: range-based approaches and range-free approaches. Range-based approaches [13, 17, 19, 24] compute per-node location information iteratively or recursively based on measured distances among target nodes and a few anchors which precisely know their locations. These approaches generally require costly hardware (e.g., GPS) and have limited effective range due to energy constraints (e.g., ultrasound-based TDOA [3, 17]). Although range-based solutions can be suitably used in small-scale indoor environments, they are considered less cost-effective for large-scale deployments. On the other hand, range-free approaches [4, 8, 10, 13, 14, 15] do not require accurate distance measurements, but localize the node based on network connectivity (proximity) information. Unfortunately, since wireless connectivity is highly influenced by the environment and hardware calibration, existing solutions fail to deliver encouraging empirical results, or require substantial survey [2] and calibration [24] on a case-by-case basis. Realizing the impracticality of existing solutions for the large-scale outdoor environment, researchers have recently proposed solutions (e.g., Spotlight [20] and Lighthouse [18]) for sensor node localization using the spatiotemporal correlation of controlled events (i.e., inferring nodes" locations based on the detection time of controlled events). These solutions demonstrate that long range and high accuracy localization can be achieved simultaneously with little additional cost at sensor nodes. These benefits, however, come along with an implicit assumption that the controlled events can be precisely distributed to a specified location at a specified time. We argue that precise event distribution is difficult to achieve, especially at large scale when terrain is uneven, the event distribution device is not well calibrated and its position is difficult to maintain (e.g., the helicopter-mounted scenario in [20]). To address these limitations in current approaches, in this paper we present a multi-sequence positioning (MSP) method 15 for large-scale stationary sensor node localization, in deployments where an event source has line-of-sight to all sensors. The novel idea behind MSP is to estimate each sensor node"s two-dimensional location by processing multiple easy-to-get one-dimensional node sequences (e.g., event detection order) obtained through loosely-guided event distribution. This design offers several benefits. First, compared to a range-based approach, MSP does not require additional costly hardware. It works using sensors typically used by sensor network applications, such as light and acoustic sensors, both of which we specifically consider in this work. Second, compared to a range-free approach, MSP needs only a small number of anchors (theoretically, as few as two), so high accuracy can be achieved economically by introducing more events instead of more anchors. And third, compared to Spotlight, MSP does not require precise and sophisticated event distribution, an advantage that significantly simplifies the system design and reduces calibration cost. This paper offers the following additional intellectual contributions: • We are the first to localize sensor nodes using the concept of node sequence, an ordered list of sensor nodes, sorted by the detection time of a disseminated event. We demonstrate that making full use of the information embedded in one-dimensional node sequences can significantly improve localization accuracy. Interestingly, we discover that repeated reprocessing of one-dimensional node sequences can further increase localization accuracy. • We propose a distribution-based location estimation strategy that obtains the final location of sensor nodes using the marginal probability of joint distribution among adjacent nodes within the sequence. This new algorithm outperforms the widely adopted Centroid estimation [4, 8]. • To the best of our knowledge, this is the first work to improve the localization accuracy of nodes by adaptive events. The generation of later events is guided by localization results from previous events. • We evaluate line-based MSP on our new Mirage test-bed, and wave-based MSP in outdoor environments. Through system implementation, we discover and address several interesting issues such as partial sequence and sequence flips. To reveal MSP performance at scale, we provide analytic results as well as a complete simulation study. All the simulation and implementation code is available online at http://www.cs.umn.edu/∼zhong/MSP. The rest of the paper is organized as follows. Section 2 briefly surveys the related work. Section 3 presents an overview of the MSP localization system. In sections 4 and 5, basic MSP and four advanced processing methods are introduced. Section 6 describes how MSP can be applied in a wave propagation scenario. Section 7 discusses several implementation issues. Section 8 presents simulation results, and Section 9 reports an evaluation of MSP on the Mirage test-bed and an outdoor test-bed. Section 10 concludes the paper. 2 Related Work Many methods have been proposed to localize wireless sensor devices in the open air. Most of these can be classified into two categories: range-based and range-free localization. Range-based localization systems, such as GPS [23], Cricket [17], AHLoS [19], AOA [16], Robust Quadrilaterals [13] and Sweeps [7], are based on fine-grained point-topoint distance estimation or angle estimation to identify pernode location. Constraints on the cost, energy and hardware footprint of each sensor node make these range-based methods undesirable for massive outdoor deployment. In addition, ranging signals generated by sensor nodes have a very limited effective range because of energy and form factor concerns. For example, ultrasound signals usually effectively propagate 20-30 feet using an on-board transmitter [17]. Consequently, these range-based solutions require an undesirably high deployment density. Although the received signal strength indicator (RSSI) related [2, 24] methods were once considered an ideal low-cost solution, the irregularity of radio propagation [26] seriously limits the accuracy of such systems. The recently proposed RIPS localization system [11] superimposes two RF waves together, creating a low-frequency envelope that can be accurately measured. This ranging technique performs very well as long as antennas are well oriented and environmental factors such as multi-path effects and background noise are sufficiently addressed. Range-free methods don"t need to estimate or measure accurate distances or angles. Instead, anchors or controlled-event distributions are used for node localization. Range-free methods can be generally classified into two types: anchor-based and anchor-free solutions. • For anchor-based solutions such as Centroid [4], APIT [8], SeRLoc [10], Gradient [13] , and APS [15], the main idea is that the location of each node is estimated based on the known locations of the anchor nodes. Different anchor combinations narrow the areas in which the target nodes can possibly be located. Anchor-based solutions normally require a high density of anchor nodes so as to achieve good accuracy. In practice, it is desirable to have as few anchor nodes as possible so as to lower the system cost. • Anchor-free solutions require no anchor nodes. Instead, external event generators and data processing platforms are used. The main idea is to correlate the event detection time at a sensor node with the known space-time relationship of controlled events at the generator so that detection time-stamps can be mapped into the locations of sensors. Spotlight [20] and Lighthouse [18] work in this fashion. In Spotlight [20], the event distribution needs to be precise in both time and space. Precise event distribution is difficult to achieve without careful calibration, especially when the event-generating devices require certain mechanical maneuvers (e.g., the telescope mount used in Spotlight). All these increase system cost and reduce localization speed. StarDust [21], which works much faster, uses label relaxation algorithms to match light spots reflected by corner-cube retro-reflectors (CCR) with sensor nodes using various constraints. Label relaxation algorithms converge only when a sufficient number of robust constraints are obtained. Due to the environmental impact on RF connectivity constraints, however, StarDust is less accurate than Spotlight. In this paper, we propose a balanced solution that avoids the limitations of both anchor-based and anchor-free solutions. Unlike anchor-based solutions [4, 8], MSP allows a flexible tradeoff between the physical cost (anchor nodes) with the soft 16 1 A B 2 3 4 5 Target nodeAnchor node 1A 5 3 B2 4 1 B2 5A 43 1A25B4 3 1 52 AB 4 3 1 2 3 5 4 (b) (c)(d) (a) Event 1 Node Sequence generated by event 1 Event 3 Node Sequence generated by event 2 Node Sequence generated by event 3 Node Sequence generated by event 4 Event 2 Event 4 Figure 1. The MSP System Overview cost (localization events). MSP uses only a small number of anchors (theoretically, as few as two). Unlike anchor-free solutions, MSP doesn"t need to maintain rigid time-space relationships while distributing events, which makes system design simpler, more flexible and more robust to calibration errors. 3 System Overview MSP works by extracting relative location information from multiple simple one-dimensional orderings of nodes. Figure 1(a) shows a layout of a sensor network with anchor nodes and target nodes. Target nodes are defined as the nodes to be localized. Briefly, the MSP system works as follows. First, events are generated one at a time in the network area (e.g., ultrasound propagations from different locations, laser scans with diverse angles). As each event propagates, as shown in Figure 1(a), each node detects it at some particular time instance. For a single event, we call the ordering of nodes, which is based on the sequential detection of the event, a node sequence. Each node sequence includes both the targets and the anchors as shown in Figure 1(b). Second, a multi-sequence processing algorithm helps to narrow the possible location of each node to a small area (Figure 1(c)). Finally, a distributionbased estimation method estimates the exact location of each sensor node, as shown in Figure 1(d). Figure 1 shows that the node sequences can be obtained much more economically than accurate pair-wise distance measurements between target nodes and anchor nodes via ranging methods. In addition, this system does not require a rigid time-space relationship for the localization events, which is critical but hard to achieve in controlled event distribution scenarios (e.g., Spotlight [20]). For the sake of clarity in presentation, we present our system in two cases: • Ideal Case, in which all the node sequences obtained from the network are complete and correct, and nodes are time-synchronized [12, 9]. • Realistic Deployment, in which (i) node sequences can be partial (incomplete), (ii) elements in sequences could flip (i.e., the order obtained is reversed from reality), and (iii) nodes are not time-synchronized. To introduce the MSP algorithm, we first consider a simple straight-line scan scenario. Then, we describe how to implement straight-line scans as well as other event types, such as sound wave propagation. 1 A 2 3 4 5 B C 6 7 8 9 Straight-line Scan 1 Straight-lineScan2 8 1 5 A 6 C 4 3 7 2 B 9 3 1 C 5 9 2 A 4 6 B 7 8 Target node Anchor node Figure 2. Obtaining Multiple Node Sequences 4 Basic MSP Let us consider a sensor network with N target nodes and M anchor nodes randomly deployed in an area of size S. The top-level idea for basic MSP is to split the whole sensor network area into small pieces by processing node sequences. Because the exact locations of all the anchors in a node sequence are known, all the nodes in this sequence can be divided into O(M +1) parts in the area. In Figure 2, we use numbered circles to denote target nodes and numbered hexagons to denote anchor nodes. Basic MSP uses two straight lines to scan the area from different directions, treating each scan as an event. All the nodes react to the event sequentially generating two node sequences. For vertical scan 1, the node sequence is (8,1,5,A,6,C,4,3,7,2,B,9), as shown outside the right boundary of the area in Figure 2; for horizontal scan 2, the node sequence is (3,1,C,5,9,2,A,4,6,B,7,8), as shown under the bottom boundary of the area in Figure 2. Since the locations of the anchor nodes are available, the anchor nodes in the two node sequences actually split the area vertically and horizontally into 16 parts, as shown in Figure 2. To extend this process, suppose we have M anchor nodes and perform d scans from different angles, obtaining d node sequences and dividing the area into many small parts. Obviously, the number of parts is a function of the number of anchors M, the number of scans d, the anchors" location as well as the slop k for each scan line. According to the pie-cutting theorem [22], the area can be divided into O(M2d2) parts. When M and d are appropriately large, the polygon for each target node may become sufficiently small so that accurate estimation can be achieved. We emphasize that accuracy is affected not only by the number of anchors M, but also by the number of events d. In other words, MSP provides a tradeoff between the physical cost of anchors and the soft cost of events. Algorithm 1 depicts the computing architecture of basic MSP. Each node sequence is processed within line 1 to 8. For each node, GetBoundaries() in line 5 searches for the predecessor and successor anchors in the sequence so as to determine the boundaries of this node. Then in line 6 UpdateMap() shrinks the location area of this node according to the newly obtained boundaries. After processing all sequences, Centroid Estimation (line 11) set the center of gravity of the final polygon as the estimated location of the target node. Basic MSP only makes use of the order information between a target node and the anchor nodes in each sequence. Actually, we can extract much more location information from 17 Algorithm 1 Basic MSP Process Output: The estimated location of each node. 1: repeat 2: GetOneUnprocessedSeqence(); 3: repeat 4: GetOneNodeFromSequenceInOrder(); 5: GetBoundaries(); 6: UpdateMap(); 7: until All the target nodes are updated; 8: until All the node sequences are processed; 9: repeat 10: GetOneUnestimatedNode(); 11: CentroidEstimation(); 12: until All the target nodes are estimated; each sequence. Section 5 will introduce advanced MSP, in which four novel optimizations are proposed to improve the performance of MSP significantly. 5 Advanced MSP Four improvements to basic MSP are proposed in this section. The first three improvements do not need additional sensing and communication in the networks but require only slightly more off-line computation. The objective of all these improvements is to make full use of the information embedded in the node sequences. The results we have obtained empirically indicate that the implementation of the first two methods can dramatically reduce the localization error, and that the third and fourth methods are helpful for some system deployments. 5.1 Sequence-Based MSP As shown in Figure 2, each scan line and M anchors, splits the whole area into M + 1 parts. Each target node falls into one polygon shaped by scan lines. We noted that in basic MSP, only the anchors are used to narrow down the polygon of each target node, but actually there is more information in the node sequence that we can made use of. Let"s first look at a simple example shown in Figure 3. The previous scans narrow the locations of target node 1 and node 2 into two dashed rectangles shown in the left part of Figure 3. Then a new scan generates a new sequence (1, 2). With knowledge of the scan"s direction, it is easy to tell that node 1 is located to the left of node 2. Thus, we can further narrow the location area of node 2 by eliminating the shaded part of node 2"s rectangle. This is because node 2 is located on the right of node 1 while the shaded area is outside the lower boundary of node 1. Similarly, the location area of node 1 can be narrowed by eliminating the shaded part out of node 2"s right boundary. We call this procedure sequence-based MSP which means that the whole node sequence needs to be processed node by node in order. Specifically, sequence-based MSP follows this exact processing rule: 1 2 1 2 1 2 Lower boundary of 1 Upper boundary of 1 Lower boundary of 2 Upper boundary of 2 New sequence New upper boundary of 1 New Lower boundary of 2 EventPropagation Figure 3. Rule Illustration in Sequence Based MSP Algorithm 2 Sequence-Based MSP Process Output: The estimated location of each node. 1: repeat 2: GetOneUnprocessedSeqence(); 3: repeat 4: GetOneNodeByIncreasingOrder(); 5: ComputeLowbound(); 6: UpdateMap(); 7: until The last target node in the sequence; 8: repeat 9: GetOneNodeByDecreasingOrder(); 10: ComputeUpbound(); 11: UpdateMap(); 12: until The last target node in the sequence; 13: until All the node sequences are processed; 14: repeat 15: GetOneUnestimatedNode(); 16: CentroidEstimation(); 17: until All the target nodes are estimated; Elimination Rule: Along a scanning direction, the lower boundary of the successor"s area must be equal to or larger than the lower boundary of the predecessor"s area, and the upper boundary of the predecessor"s area must be equal to or smaller than the upper boundary of the successor"s area. In the case of Figure 3, node 2 is the successor of node 1, and node 1 is the predecessor of node 2. According to the elimination rule, node 2"s lower boundary cannot be smaller than that of node 1 and node 1"s upper boundary cannot exceed node 2"s upper boundary. Algorithm 2 illustrates the pseudo code of sequence-based MSP. Each node sequence is processed within line 3 to 13. The sequence processing contains two steps: Step 1 (line 3 to 7): Compute and modify the lower boundary for each target node by increasing order in the node sequence. Each node"s lower boundary is determined by the lower boundary of its predecessor node in the sequence, thus the processing must start from the first node in the sequence and by increasing order. Then update the map according to the new lower boundary. Step 2 (line 8 to 12): Compute and modify the upper boundary for each node by decreasing order in the node sequence. Each node"s upper boundary is determined by the upper boundary of its successor node in the sequence, thus the processing must start from the last node in the sequence and by decreasing order. Then update the map according to the new upper boundary. After processing all the sequences, for each node, a polygon bounding its possible location has been found. Then, center-ofgravity-based estimation is applied to compute the exact location of each node (line 14 to 17). An example of this process is shown in Figure 4. The third scan generates the node sequence (B,9,2,7,4,6,3,8,C,A,5,1). In addition to the anchor split lines, because nodes 4 and 7 come after node 2 in the sequence, node 4 and 7"s polygons could be narrowed according to node 2"s lower boundary (the lower right-shaded area); similarly, the shaded area in node 2"s rectangle could be eliminated since this part is beyond node 7"s upper boundary indicated by the dotted line. Similar eliminating can be performed for node 3 as shown in the figure. 18 1 A 2 3 4 5 B C 6 7 8 9 Straight-line Scan 1 Straight-lineScan2 Straight-line Scan 3 Target node Anchor node Figure 4. Sequence-Based MSP Example 1 A 2 3 4 5 B C 6 7 8 9 Straight-line Scan 1 Straight-lineScan2 Straight-line Scan 3 Reprocessing Scan 1 Target node Anchor node Figure 5. Iterative MSP: Reprocessing Scan 1 From above, we can see that the sequence-based MSP makes use of the information embedded in every sequential node pair in the node sequence. The polygon boundaries of the target nodes obtained in prior could be used to further split other target nodes" areas. Our evaluation in Sections 8 and 9 shows that sequence-based MSP considerably enhances system accuracy. 5.2 Iterative MSP Sequence-based MSP is preferable to basic MSP because it extracts more information from the node sequence. In fact, further useful information still remains! In sequence-based MSP, a sequence processed later benefits from information produced by previously processed sequences (e.g., the third scan in Figure 5). However, the first several sequences can hardly benefit from other scans in this way. Inspired by this phenomenon, we propose iterative MSP. The basic idea of iterative MSP is to process all the sequences iteratively several times so that the processing of each single sequence can benefit from the results of other sequences. To illustrate the idea more clearly, Figure 4 shows the results of three scans that have provided three sequences. Now if we process the sequence (8,1,5,A,6,C,4,3,7,2,B,9) obtained from scan 1 again, we can make progress, as shown in Figure 5. The reprocessing of the node sequence 1 provides information in the way an additional vertical scan would. From sequencebased MSP, we know that the upper boundaries of nodes 3 and 4 along the scan direction must not extend beyond the upper boundary of node 7, therefore the grid parts can be eliminated (a) Central of Gravity (b) Joint Distribution 1 2 2 1 1 2 1 2 2 1 1 2 2 1 1 2 Figure 6. Example of Joint Distribution Estimation …... vm ap[0] vm ap[1] vm ap[2] vm ap[3] Combine m ap Figure 7. Idea of DBE MSP for Each Node for the nodes 3 and node 4, respectively, as shown in Figure 5. From this example, we can see that iterative processing of the sequence could help further shrink the polygon of each target node, and thus enhance the accuracy of the system. The implementation of iterative MSP is straightforward: process all the sequences multiple times using sequence-based MSP. Like sequence-based MSP, iterative MSP introduces no additional event cost. In other words, reprocessing does not actually repeat the scan physically. Evaluation results in Section 8 will show that iterative MSP contributes noticeably to a lower localization error. Empirical results show that after 5 iterations, improvements become less significant. In summary, iterative processing can achieve better performance with only a small computation overhead. 5.3 Distribution-Based Estimation After determining the location area polygon for each node, estimation is needed for a final decision. Previous research mostly applied the Center of Gravity (COG) method [4] [8] [10] which minimizes average error. If every node is independent of all others, COG is the statistically best solution. In MSP, however, each node may not be independent. For example, two neighboring nodes in a certain sequence could have overlapping polygon areas. In this case, if the marginal probability of joint distribution is used for estimation, better statistical results are achieved. Figure 6 shows an example in which node 1 and node 2 are located in the same polygon. If COG is used, both nodes are localized at the same position (Figure 6(a)). However, the node sequences obtained from two scans indicate that node 1 should be to the left of and above node 2, as shown in Figure 6(b). The high-level idea of distribution-based estimation proposed for MSP, which we call DBE MSP, is illustrated in Figure 7. The distributions of each node under the ith scan (for the ith node sequence) are estimated in node.vmap[i], which is a data structure for remembering the marginal distribution over scan i. Then all the vmaps are combined to get a single map and weighted estimation is used to obtain the final location. For each scan, all the nodes are sorted according to the gap, which is the diameter of the polygon along the direction of the scan, to produce a second, gap-based node sequence. Then, the estimation starts from the node with the smallest gap. This is because it is statistically more accurate to assume a uniform distribution of the node with smaller gap. For each node processed in order from the gap-based node sequence, either if 19 Pred. node"s area Predecessor node exists: conditional distribution based on pred. node"s area Alone: Uniformly Distributed Succ. node"s area Successor node exists: conditional distribution based on succ. node"s area Succ. node"s area Both predecessor and successor nodes exist: conditional distribution based on both of them Pred. node"s area Figure 8. Four Cases in DBE Process no neighbor node in the original event-based node sequence shares an overlapping area, or if the neighbors have not been processed due to bigger gaps, a uniform distribution Uniform() is applied to this isolated node (the Alone case in Figure 8). If the distribution of its neighbors sharing overlapped areas has been processed, we calculate the joint distribution for the node. As shown in Figure 8, there are three possible cases depending on whether the distribution of the overlapping predecessor and/or successor nodes have/has already been estimated. The estimation"s strategy of starting from the most accurate node (smallest gap node) reduces the problem of estimation error propagation. The results in the evaluation section indicate that applying distribution-based estimation could give statistically better results. 5.4 Adaptive MSP So far, all the enhancements to basic MSP focus on improving the multi-sequence processing algorithm given a fixed set of scan directions. All these enhancements require only more computing time without any overhead to the sensor nodes. Obviously, it is possible to have some choice and optimization on how events are generated. For example, in military situations, artillery or rocket-launched mini-ultrasound bombs can be used for event generation at some selected locations. In adaptive MSP, we carefully generate each new localization event so as to maximize the contribution of the new event to the refinement of localization, based on feedback from previous events. Figure 9 depicts the basic architecture of adaptive MSP. Through previous localization events, the whole map has been partitioned into many small location areas. The idea of adaptive MSP is to generate the next localization event to achieve best-effort elimination, which ideally could shrink the location area of individual node as much as possible. We use a weighted voting mechanism to evaluate candidate localization events. Every node wants the next event to split its area evenly, which would shrink the area fast. Therefore, every node votes for the parameters of the next event (e.g., the scan angle k of the straight-line scan). Since the area map is maintained centrally, the vote is virtually done and there is no need for the real sensor nodes to participate in it. After gathering all the voting results, the event parameters with the most votes win the election. There are two factors that determine the weight of each vote: • The vote for each candidate event is weighted according to the diameter D of the node"s location area. Nodes with bigger location areas speak louder in the voting, because Map Partitioned by the Localization Events Diameter of Each Area Candidate Localization Events Evaluation Trigger Next Localization Evet Figure 9. Basic Architecture of Adaptive MSP 2 3 Diameter D3 1 1 3k 2 3k 3 3k 4 3k 5 3k 6 3k 1 3k 2 3k 3 3k 6 3k4 3k 5 3k Weight el small i opt i j ii j i S S DkkDfkWeight arg ),(,()( ⋅=∆= 1 3 opt k Target node Anchor node Center of Gravity Node 3's area Figure 10. Candidate Slops for Node 3 at Anchor 1 overall system error is reduced mostly by splitting the larger areas. • The vote for each candidate event is also weighted according to its elimination efficiency for a location area, which is defined as how equally in size (or in diameter) an event can cut an area. In other words, an optimal scan event cuts an area in the middle, since this cut shrinks the area quickly and thus reduces localization uncertainty quickly. Combining the above two aspects, the weight for each vote is computed according to the following equation (1): Weight(k j i ) = f(Di,△(k j i ,k opt i )) (1) k j i is node i"s jth supporting parameter for next event generation; Di is diameter of node i"s location area; △(k j i ,k opt i ) is the distance between k j i and the optimal parameter k opt i for node i, which should be defined to fit the specific application. Figure 10 presents an example for node 1"s voting for the slopes of the next straight-line scan. In the system, there are a fixed number of candidate slopes for each scan (e.g., k1,k2,k3,k4...). The location area of target node 3 is shown in the figure. The candidate events k1 3,k2 3,k3 3,k4 3,k5 3,k6 3 are evaluated according to their effectiveness compared to the optimal ideal event which is shown as a dotted line with appropriate weights computed according to equation (1). For this specific example, as is illustrated in the right part of Figure 10, f(Di,△(k j i ,kopt i )) is defined as the following equation (2): Weight(kj i ) = f(Di,△(kj i ,kopt i )) = Di · Ssmall Slarge (2) Ssmall and Slarge are the sizes of the smaller part and larger part of the area cut by the candidate line respectively. In this case, node 3 votes 0 for the candidate lines that do not cross its area since Ssmall = 0. We show later that adaptive MSP improves localization accuracy in WSNs with irregularly shaped deployment areas. 20 5.5 Overhead and MSP Complexity Analysis This section provides a complexity analysis of the MSP design. We emphasize that MSP adopts an asymmetric design in which sensor nodes need only to detect and report the events. They are blissfully oblivious to the processing methods proposed in previous sections. In this section, we analyze the computational cost on the node sequence processing side, where resources are plentiful. According to Algorithm 1, the computational complexity of Basic MSP is O(d · N · S), and the storage space required is O(N · S), where d is the number of events, N is the number of target nodes, and S is the area size. According to Algorithm 2, the computational complexity of both sequence-based MSP and iterative MSP is O(c·d ·N ·S), where c is the number of iterations and c = 1 for sequencebased MSP, and the storage space required is O(N ·S). Both the computational complexity and storage space are equal within a constant factor to those of basic MSP. The computational complexity of the distribution-based estimation (DBE MSP) is greater. The major overhead comes from the computation of joint distributions when both predecessor and successor nodes exit. In order to compute the marginal probability, MSP needs to enumerate the locations of the predecessor node and the successor node. For example, if node A has predecessor node B and successor node C, then the marginal probability PA(x,y) of node A"s being at location (x,y) is: PA(x,y) = ∑ i ∑ j ∑ m ∑ n 1 NB,A,C ·PB(i, j)·PC(m,n) (3) NB,A,C is the number of valid locations for A satisfying the sequence (B, A, C) when B is at (i, j) and C is at (m,n); PB(i, j) is the available probability of node B"s being located at (i, j); PC(m,n) is the available probability of node C"s being located at (m,n). A naive algorithm to compute equation (3) has complexity O(d · N · S3). However, since the marginal probability indeed comes from only one dimension along the scanning direction (e.g., a line), the complexity can be reduced to O(d · N · S1.5) after algorithm optimization. In addition, the final location areas for every node are much smaller than the original field S; therefore, in practice, DBE MSP can be computed much faster than O(d ·N ·S1.5). 6 Wave Propagation Example So far, the description of MSP has been solely in the context of straight-line scan. However, we note that MSP is conceptually independent of how the event is propagated as long as node sequences can be obtained. Clearly, we can also support wave-propagation-based events (e.g., ultrasound propagation, air blast propagation), which are polar coordinate equivalences of the line scans in the Cartesian coordinate system. This section illustrates the effects of MSP"s implementation in the wave propagation-based situation. For easy modelling, we have made the following assumptions: • The wave propagates uniformly in all directions, therefore the propagation has a circle frontier surface. Since MSP does not rely on an accurate space-time relationship, a certain distortion in wave propagation is tolerable. If any directional wave is used, the propagation frontier surface can be modified accordingly. 1 3 5 9 Target node Anchor node Previous Event location A 2 Center of Gravity 4 8 7 B 6 C A line of preferred locations for next event Figure 11. Example of Wave Propagation Situation • Under the situation of line-of-sight, we allow obstacles to reflect or deflect the wave. Reflection and deflection are not problems because each node reacts only to the first detected event. Those reflected or deflected waves come later than the line-of-sight waves. The only thing the system needs to maintain is an appropriate time interval between two successive localization events. • We assume that background noise exists, and therefore we run a band-pass filter to listen to a particular wave frequency. This reduces the chances of false detection. The parameter that affects the localization event generation here is the source location of the event. The different distances between each node and the event source determine the rank of each node in the node sequence. Using the node sequences, the MSP algorithm divides the whole area into many non-rectangular areas as shown in Figure 11. In this figure, the stars represent two previous event sources. The previous two propagations split the whole map into many areas by those dashed circles that pass one of the anchors. Each node is located in one of the small areas. Since sequence-based MSP, iterative MSP and DBE MSP make no assumptions about the type of localization events and the shape of the area, all three optimization algorithms can be applied for the wave propagation scenario. However, adaptive MSP needs more explanation. Figure 11 illustrates an example of nodes" voting for next event source locations. Unlike the straight-line scan, the critical parameter now is the location of the event source, because the distance between each node and the event source determines the rank of the node in the sequence. In Figure 11, if the next event breaks out along/near the solid thick gray line, which perpendicularly bisects the solid dark line between anchor C and the center of gravity of node 9"s area (the gray area), the wave would reach anchor C and the center of gravity of node 9"s area at roughly the same time, which would relatively equally divide node 9"s area. Therefore, node 9 prefers to vote for the positions around the thick gray line. 7 Practical Deployment Issues For the sake of presentation, until now we have described MSP in an ideal case where a complete node sequence can be obtained with accurate time synchronization. In this section we describe how to make MSP work well under more realistic conditions. 21 7.1 Incomplete Node Sequence For diverse reasons, such as sensor malfunction or natural obstacles, the nodes in the network could fail to detect localization events. In such cases, the node sequence will not be complete. This problem has two versions: • Anchor nodes are missing in the node sequence If some anchor nodes fail to respond to the localization events, then the system has fewer anchors. In this case, the solution is to generate more events to compensate for the loss of anchors so as to achieve the desired accuracy requirements. • Target nodes are missing in the node sequence There are two consequences when target nodes are missing. First, if these nodes are still be useful to sensing applications, they need to use other backup localization approaches (e.g., Centroid) to localize themselves with help from their neighbors who have already learned their own locations from MSP. Secondly, since in advanced MSP each node in the sequence may contribute to the overall system accuracy, dropping of target nodes from sequences could also reduce the accuracy of the localization. Thus, proper compensation procedures such as adding more localization events need to be launched. 7.2 Localization without Time Synchronization In a sensor network without time synchronization support, nodes cannot be ordered into a sequence using timestamps. For such cases, we propose a listen-detect-assemble-report protocol, which is able to function independently without time synchronization. listen-detect-assemble-report requires that every node listens to the channel for the node sequence transmitted from its neighbors. Then, when the node detects the localization event, it assembles itself into the newest node sequence it has heard and reports the updated sequence to other nodes. Figure 12 (a) illustrates an example for the listen-detect-assemble-report protocol. For simplicity, in this figure we did not differentiate the target nodes from anchor nodes. A solid line between two nodes stands for a communication link. Suppose a straight line scans from left to right. Node 1 detects the event, and then it broadcasts the sequence (1) into the network. Node 2 and node 3 receive this sequence. When node 2 detects the event, node 2 adds itself into the sequence and broadcasts (1, 2). The sequence propagates in the same direction with the scan as shown in Figure 12 (a). Finally, node 6 obtains a complete sequence (1,2,3,5,7,4,6). In the case of ultrasound propagation, because the event propagation speed is much slower than that of radio, the listendetect-assemble-report protocol can work well in a situation where the node density is not very high. For instance, if the distance between two nodes along one direction is 10 meters, the 340m/s sound needs 29.4ms to propagate from one node to the other. While normally the communication data rate is 250Kbps in the WSN (e.g., CC2420 [1]), it takes only about 2 ∼ 3 ms to transmit an assembled packet for one hop. One problem that may occur using the listen-detectassemble-report protocol is multiple partial sequences as shown in Figure 12 (b). Two separate paths in the network may result in two sequences that could not be further combined. In this case, since the two sequences can only be processed as separate sequences, some order information is lost. Therefore the 1,2,5,4 1,3,7,4 1,2,3,5 1,2,3,5,7,4 1,2,3,5,7 1,2,3,5 1,3 1,2 1 2 3 5 7 4 6 1 1 1,3 1,2,3,5,7,4,6 1,2,5 1,3,7 1,3 1,2 1 2 3 5 7 4 6 1 1 1,3,7,4,6 1,2,5,4,6 (a) (b) (c) 1,3,2,5 1,3,2,5,7,4 1,3,2,5,7 1,3,2,5 1,3 1,2 1 2 3 5 7 4 6 1 1 1,3 1,3,2,5,7,4,6 Event Propagation Event Propagation Event Propagation Figure 12. Node Sequence without Time Synchronization accuracy of the system would decrease. The other problem is the sequence flip problem. As shown in Figure 12 (c), because node 2 and node 3 are too close to each other along the scan direction, they detect the scan almost simultaneously. Due to the uncertainty such as media access delay, two messages could be transmitted out of order. For example, if node 3 sends out its report first, then the order of node 2 and node 3 gets flipped in the final node sequence. The sequence flip problem would appear even in an accurately synchronized system due to random jitter in node detection if an event arrives at multiple nodes almost simultaneously. A method addressing the sequence flip is presented in the next section. 7.3 Sequence Flip and Protection Band Sequence flip problems can be solved with and without time synchronization. We firstly start with a scenario applying time synchronization. Existing solutions for time synchronization [12, 6] can easily achieve sub-millisecond-level accuracy. For example, FTSP [12] achieves 16.9µs (microsecond) average error for a two-node single-hop case. Therefore, we can comfortably assume that the network is synchronized with maximum error of 1000µs. However, when multiple nodes are located very near to each other along the event propagation direction, even when time synchronization with less than 1ms error is achieved in the network, sequence flip may still occur. For example, in the sound wave propagation case, if two nodes are less than 0.34 meters apart, the difference between their detection timestamp would be smaller than 1 millisecond. We find that sequence flip could not only damage system accuracy, but also might cause a fatal error in the MSP algorithm. Figure 13 illustrates both detrimental results. In the left side of Figure 13(a), suppose node 1 and node 2 are so close to each other that it takes less than 0.5ms for the localization event to propagate from node 1 to node 2. Now unfortunately, the node sequence is mistaken to be (2,1). So node 1 is expected to be located to the right of node 2, such as at the position of the dashed node 1. According to the elimination rule in sequencebased MSP, the left part of node 1"s area is cut off as shown in the right part of Figure 13(a). This is a potentially fatal error, because node 1 is actually located in the dashed area which has been eliminated by mistake. During the subsequent eliminations introduced by other events, node 1"s area might be cut off completely, thus node 1 could consequently be erased from the map! Even in cases where node 1 still survives, its area actually does not cover its real location. 22 1 2 12 2 Lower boundary of 1 Upper boundary of 1 Flipped Sequence Fatal Elimination Error EventPropagation 1 1 Fatal Error 1 1 2 12 2 Lower boundary of 1 Upper boundary of 1 Flipped Sequence Safe Elimination EventPropagation 1 1 New lower boundary of 1 1 B (a) (b) B: Protection band Figure 13. Sequence Flip and Protection Band Another problem is not fatal but lowers the localization accuracy. If we get the right node sequence (1,2), node 1 has a new upper boundary which can narrow the area of node 1 as in Figure 3. Due to the sequence flip, node 1 loses this new upper boundary. In order to address the sequence flip problem, especially to prevent nodes from being erased from the map, we propose a protection band compensation approach. The basic idea of protection band is to extend the boundary of the location area a little bit so as to make sure that the node will never be erased from the map. This solution is based on the fact that nodes have a high probability of flipping in the sequence if they are near to each other along the event propagation direction. If two nodes are apart from each other more than some distance, say, B, they rarely flip unless the nodes are faulty. The width of a protection band B, is largely determined by the maximum error in system time synchronization and the localization event propagation speed. Figure 13(b) presents the application of the protection band. Instead of eliminating the dashed part in Figure 13(a) for node 1, the new lower boundary of node 1 is set by shifting the original lower boundary of node 2 to the left by distance B. In this case, the location area still covers node 1 and protects it from being erased. In a practical implementation, supposing that the ultrasound event is used, if the maximum error of system time synchronization is 1ms, two nodes might flip with high probability if the timestamp difference between the two nodes is smaller than or equal to 1ms. Accordingly, we set the protection band B as 0.34m (the distance sound can propagate within 1 millisecond). By adding the protection band, we reduce the chances of fatal errors, although at the cost of localization accuracy. Empirical results obtained from our physical test-bed verified this conclusion. In the case of using the listen-detect-assemble-report protocol, the only change we need to make is to select the protection band according to the maximum delay uncertainty introduced by the MAC operation and the event propagation speed. To bound MAC delay at the node side, a node can drop its report message if it experiences excessive MAC delay. This converts the sequence flip problem to the incomplete sequence problem, which can be more easily addressed by the method proposed in Section 7.1. 8 Simulation Evaluation Our evaluation of MSP was conducted on three platforms: (i) an indoor system with 46 MICAz motes using straight-line scan, (ii) an outdoor system with 20 MICAz motes using sound wave propagation, and (iii) an extensive simulation under various kinds of physical settings. In order to understand the behavior of MSP under numerous settings, we start our evaluation with simulations. Then, we implemented basic MSP and all the advanced MSP methods for the case where time synchronization is available in the network. The simulation and implementation details are omitted in this paper due to space constraints, but related documents [25] are provided online at http://www.cs.umn.edu/∼zhong/MSP. Full implementation and evaluation of system without time synchronization are yet to be completed in the near future. In simulation, we assume all the node sequences are perfect so as to reveal the performance of MSP achievable in the absence of incomplete node sequences or sequence flips. In our simulations, all the anchor nodes and target nodes are assumed to be deployed uniformly. The mean and maximum errors are averaged over 50 runs to obtain high confidence. For legibility reasons, we do not plot the confidence intervals in this paper. All the simulations are based on the straight-line scan example. We implement three scan strategies: • Random Scan: The slope of the scan line is randomly chosen at each time. • Regular Scan: The slope is predetermined to rotate uniformly from 0 degree to 180 degrees. For example, if the system scans 6 times, then the scan angles would be: 0, 30, 60, 90, 120, and 150. • Adaptive Scan: The slope of each scan is determined based on the localization results from previous scans. We start with basic MSP and then demonstrate the performance improvements one step at a time by adding (i) sequencebased MSP, (ii) iterative MSP, (iii) DBE MSP and (iv) adaptive MSP. 8.1 Performance of Basic MSP The evaluation starts with basic MSP, where we compare the performance of random scan and regular scan under different configurations. We intend to illustrate the impact of the number of anchors M, the number of scans d, and target node density (number of target nodes N in a fixed-size region) on the localization error. Table 1 shows the default simulation parameters. The error of each node is defined as the distance between the estimated location and the real position. We note that by default we only use three anchors, which is considerably fewer than existing range-free solutions [8, 4]. Impact of the Number of Scans: In this experiment, we compare regular scan with random scan under a different number of scans from 3 to 30 in steps of 3. The number of anchors Table 1. Default Configuration Parameters Parameter Description Field Area 200×200 (Grid Unit) Scan Type Regular (Default)/Random Scan Anchor Number 3 (Default) Scan Times 6 (Default) Target Node Number 100 (Default) Statistics Error Mean/Max Random Seeds 50 runs 23 0 5 10 15 20 25 30 0 10 20 30 40 50 60 70 80 90 Mean Error and Max Error VS Scan Time Scan Time Error Max Error of Random Scan Max Error of Regular Scan Mean Error of Random Scan Mean Error of Regular Scan (a) Error vs. Number of Scans 0 5 10 15 20 25 30 0 10 20 30 40 50 60 Mean Error and Max Error VS Anchor Number Anchor Number Error Max Error of Random Scan Max Error of Regular Scan Mean Error of Random Scan Mean Error of Regular Scan (b) Error vs. Anchor Number 0 50 100 150 200 10 20 30 40 50 60 70 Mean Error and Max Error VS Target Node Number Target Node Number Error Max Error of Random Scan Max Error of Regular Scan Mean Error of Random Scan Mean Error of Regular Scan (c) Error vs. Number of Target Nodes Figure 14. Evaluation of Basic MSP under Random and Regular Scans 0 5 10 15 20 25 30 0 10 20 30 40 50 60 70 Basic MSP VS Sequence Based MSP II Scan Time Error Max Error of Basic MSP Max Error of Seq MSP Mean Error of Basic MSP Mean Error of Seq MSP (a) Error vs. Number of Scans 0 5 10 15 20 25 30 0 5 10 15 20 25 30 35 40 45 50 Basic MSP VS Sequence Based MSP I Anchor Number Error Max Error of Basic MSP Max Error of Seq MSP Mean Error of Basic MSP Mean Error of Seq MSP (b) Error vs. Anchor Number 0 50 100 150 200 5 10 15 20 25 30 35 40 45 50 55 Basic MSP VS Sequence Based MSP III Target Node Number Error Max Error of Basic MSP Max Error of Seq MSP Mean Error of Basic MSP Mean Error of Seq MSP (c) Error vs. Number of Target Nodes Figure 15. Improvements of Sequence-Based MSP over Basic MSP is 3 by default. Figure 14(a) indicates the following: (i) as the number of scans increases, the localization error decreases significantly; for example, localization errors drop more than 60% from 3 scans to 30 scans; (ii) statistically, regular scan achieves better performance than random scan under identical number of scans. However, the performance gap reduces as the number of scans increases. This is expected since a large number of random numbers converges to a uniform distribution. Figure 14(a) also demonstrates that MSP requires only a small number of anchors to perform very well, compared to existing range-free solutions [8, 4]. Impact of the Number of Anchors: In this experiment, we compare regular scan with random scan under different number of anchors from 3 to 30 in steps of 3. The results shown in Figure 14(b) indicate that (i) as the number of anchor nodes increases, the localization error decreases, and (ii) statistically, regular scan obtains better results than random scan with identical number of anchors. By combining Figures 14(a) and 14(b), we can conclude that MSP allows a flexible tradeoff between physical cost (anchor nodes) and soft cost (localization events). Impact of the Target Node Density: In this experiment, we confirm that the density of target nodes has no impact on the accuracy, which motivated the design of sequence-based MSP. In this experiment, we compare regular scan with random scan under different number of target nodes from 10 to 190 in steps of 20. Results in Figure 14(c) show that mean localization errors remain constant across different node densities. However, when the number of target nodes increases, the average maximum error increases. Summary: From the above experiments, we can conclude that in basic MSP, regular scan are better than random scan under different numbers of anchors and scan events. This is because regular scans uniformly eliminate the map from different directions, while random scans would obtain sequences with redundant overlapping information, if two scans choose two similar scanning slopes. 8.2 Improvements of Sequence-Based MSP This section evaluates the benefits of exploiting the order information among target nodes by comparing sequence-based MSP with basic MSP. In this and the following sections, regular scan is used for straight-line scan event generation. The purpose of using regular scan is to keep the scan events and the node sequences identical for both sequence-based MSP and basic MSP, so that the only difference between them is the sequence processing procedure. Impact of the Number of Scans: In this experiment, we compare sequence-based MSP with basic MSP under different number of scans from 3 to 30 in steps of 3. Figure 15(a) indicates significant performance improvement in sequence-based MSP over basic MSP across all scan settings, especially when the number of scans is large. For example, when the number of scans is 30, errors in sequence-based MSP are about 20% of that of basic MSP. We conclude that sequence-based MSP performs extremely well when there are many scan events. Impact of the Number of Anchors: In this experiment, we use different number of anchors from 3 to 30 in steps of 3. As seen in Figure 15(b), the mean error and maximum error of sequence-based MSP is much smaller than that of basic MSP. Especially when there is limited number of anchors in the system, e.g., 3 anchors, the error rate was almost halved by using sequence-based MSP. This phenomenon has an interesting explanation: the cutting lines created by anchor nodes are exploited by both basic MSP and sequence-based MSP, so as the 24 0 2 4 6 8 10 0 5 10 15 20 25 30 35 40 45 50 Basic MSP VS Iterative MSP Iterative Times Error Max Error of Iterative Seq MSP Mean Error of Iterative Seq MSP Max Error of Basic MSP Mean Error of Basic MSP Figure 16. Improvements of Iterative MSP 0 2 4 6 8 10 12 14 16 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 DBE VS Non−DBE Error CumulativeDistrubutioinFunctions(CDF) Mean Error CDF of DBE MSP Mean Error CDF of Non−DBE MSP Max Error CDF of DBE MSP Max Error CDF of Non−DBE MSP Figure 17. Improvements of DBE MSP 0 20 40 60 80 100 0 10 20 30 40 50 60 70 Adaptive MSP for 500by80 Target Node Number Error Max Error of Regualr Scan Max Error of Adaptive Scan Mean Error of Regualr Scan Mean Error of Adaptive Scan (a) Adaptive MSP for 500 by 80 field 0 10 20 30 40 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean Error CDF at Different Angle Steps in Adaptive Scan Mean Error CumulativeDistrubutioinFunctions(CDF) 5 Degree Angle Step Adaptive 10 Degree Angle Step Adaptive 20 Degree Angle Step Adaptive 30 Degree Step Regular Scan (b) Impact of the Number of Candidate Events Figure 18. The Improvements of Adaptive MSP number of anchor nodes increases, anchors tend to dominate the contribution. Therefore the performance gaps lessens. Impact of the Target Node Density: Figure 15(c) demonstrates the benefits of exploiting order information among target nodes. Since sequence-based MSP makes use of the information among the target nodes, having more target nodes contributes to the overall system accuracy. As the number of target nodes increases, the mean error and maximum error of sequence-based MSP decreases. Clearly the mean error in basic MSP is not affected by the number of target nodes, as shown in Figure 15(c). Summary: From the above experiments, we can conclude that exploiting order information among target nodes can improve accuracy significantly, especially when the number of events is large but with few anchors. 8.3 Iterative MSP over Sequence-Based MSP In this experiment, the same node sequences were processed iteratively multiple times. In Figure 16, the two single marks are results from basic MSP, since basic MSP doesn"t perform iterations. The two curves present the performance of iterative MSP under different numbers of iterations c. We note that when only a single iteration is used, this method degrades to sequence-based MSP. Therefore, Figure 16 compares the three methods to one another. Figure 16 shows that the second iteration can reduce the mean error and maximum error dramatically. After that, the performance gain gradually reduces, especially when c > 5. This is because the second iteration allows earlier scans to exploit the new boundaries created by later scans in the first iteration. Such exploitation decays quickly over iterations. 8.4 DBE MSP over Iterative MSP Figure 17, in which we augment iterative MSP with distribution-based estimation (DBE MSP), shows that DBE MSP could bring about statistically better performance. Figure 17 presents cumulative distribution localization errors. In general, the two curves of the DBE MSP lay slightly to the left of that of non-DBE MSP, which indicates that DBE MSP has a smaller statistical mean error and averaged maximum error than non-DBE MSP. We note that because DBE is augmented on top of the best solution so far, the performance improvement is not significant. When we apply DBE on basic MSP methods, the improvement is much more significant. We omit these results because of space constraints. 8.5 Improvements of Adaptive MSP This section illustrates the performance of adaptive MSP over non-adaptive MSP. We note that feedback-based adaptation can be applied to all MSP methods, since it affects only the scanning angles but not the sequence processing. In this experiment, we evaluated how adaptive MSP can improve the best solution so far. The default angle granularity (step) for adaptive searching is 5 degrees. Impact of Area Shape: First, if system settings are regular, the adaptive method hardly contributes to the results. For a square area (regular), the performance of adaptive MSP and regular scans are very close. However, if the shape of the area is not regular, adaptive MSP helps to choose the appropriate localization events to compensate. Therefore, adaptive MSP can achieve a better mean error and maximum error as shown in Figure 18(a). For example, adaptive MSP improves localization accuracy by 30% when the number of target nodes is 10. Impact of the Target Node Density: Figure 18(a) shows that when the node density is low, adaptive MSP brings more benefit than when node density is high. This phenomenon makes statistical sense, because the law of large numbers tells us that node placement approaches a truly uniform distribution when the number of nodes is increased. Adaptive MSP has an edge 25 Figure 19. The Mirage Test-bed (Line Scan) Figure 20. The 20-node Outdoor Experiments (Wave) when layout is not uniform. Impact of Candidate Angle Density: Figure 18(b) shows that the smaller the candidate scan angle step, the better the statistical performance in terms of mean error. The rationale is clear, as wider candidate scan angles provide adaptive MSP more opportunity to choose the one approaching the optimal angle. 8.6 Simulation Summary Starting from basic MSP, we have demonstrated step-bystep how four optimizations can be applied on top of each other to improve localization performance. In other words, these optimizations are compatible with each other and can jointly improve the overall performance. We note that our simulations were done under assumption that the complete node sequence can be obtained without sequence flips. In the next section, we present two real-system implementations that reveal and address these practical issues. 9 System Evaluation In this section, we present a system implementation of MSP on two physical test-beds. The first one is called Mirage, a large indoor test-bed composed of six 4-foot by 8-foot boards, illustrated in Figure 19. Each board in the system can be used as an individual sub-system, which is powered, controlled and metered separately. Three Hitachi CP-X1250 projectors, connected through a Matorx Triplehead2go graphics expansion box, are used to create an ultra-wide integrated display on six boards. Figure 19 shows that a long tilted line is generated by the projectors. We have implemented all five versions of MSP on the Mirage test-bed, running 46 MICAz motes. Unless mentioned otherwise, the default setting is 3 anchors and 6 scans at the scanning line speed of 8.6 feet/s. In all of our graphs, each data point represents the average value of 50 trials. In the outdoor system, a Dell A525 speaker is used to generate 4.7KHz sound as shown in Figure 20. We place 20 MICAz motes in the backyard of a house. Since the location is not completely open, sound waves are reflected, scattered and absorbed by various objects in the vicinity, causing a multi-path effect. In the system evaluation, simple time synchronization mechanisms are applied on each node. 9.1 Indoor System Evaluation During indoor experiments, we encountered several realworld problems that are not revealed in the simulation. First, sequences obtained were partial due to misdetection and message losses. Second, elements in the sequences could flip due to detection delay, uncertainty in media access, or error in time synchronization. We show that these issues can be addressed by using the protection band method described in Section 7.3. 9.1.1 On Scanning Speed and Protection Band In this experiment, we studied the impact of the scanning speed and the length of protection band on the performance of the system. In general, with increasing scanning speed, nodes have less time to respond to the event and the time gap between two adjacent nodes shrinks, leading to an increasing number of partial sequences and sequence flips. Figure 21 shows the node flip situations for six scans with distinct angles under different scan speeds. The x-axis shows the distance between the flipped nodes in the correct node sequence. y-axis shows the total number of flips in the six scans. This figure tells us that faster scan brings in not only increasing number of flips, but also longer-distance flips that require wider protection band to prevent from fatal errors. Figure 22(a) shows the effectiveness of the protection band in terms of reducing the number of unlocalized nodes. When we use a moderate scan speed (4.3feet/s), the chance of flipping is rare, therefore we can achieve 0.45 feet mean accuracy (Figure 22(b)) with 1.6 feet maximum error (Figure 22(c)). With increasing speeds, the protection band needs to be set to a larger value to deal with flipping. Interesting phenomena can be observed in Figures 22: on one hand, the protection band can sharply reduce the number of unlocalized nodes; on the other hand, protection bands enlarge the area in which a target would potentially reside, introducing more uncertainty. Thus there is a concave curve for both mean and maximum error when the scan speed is at 8.6 feet/s. 9.1.2 On MSP Methods and Protection Band In this experiment, we show the improvements resulting from three different methods. Figure 23(a) shows that a protection band of 0.35 feet is sufficient for the scan speed of 8.57feet/s. Figures 23(b) and 23(c) show clearly that iterative MSP (with adaptation) achieves best performance. For example, Figures 23(b) shows that when we set the protection band at 0.05 feet, iterative MSP achieves 0.7 feet accuracy, which is 42% more accurate than the basic design. Similarly, Figures 23(b) and 23(c) show the double-edged effects of protection band on the localization accuracy. 0 5 10 15 20 0 20 40 (3) Flip Distribution for 6 Scans at Line Speed of 14.6feet/s Flips Node Distance in the Ideal Node Sequence 0 5 10 15 20 0 20 40 (2) Flip Distribution for 6 Scans at Line Speed of 8.6feet/s Flips 0 5 10 15 20 0 20 40 (1) Flip Distribution for 6 Scans at Line Speed of 4.3feet/s Flips Figure 21. Number of Flips for Different Scan Speeds 26 0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 12 14 16 18 20 Unlocalized Node Number(Line Scan at Different Speed) Protection Band (in feet) UnlocalizedNodeNumber Scan Line Speed: 14.6feet/s Scan Line Speed: 8.6feet/s Scan Line Speed: 4.3feet/s (a) Number of Unlocalized Nodes 0 0.2 0.4 0.6 0.8 1 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 Mean Error(Line Scan at Different Speed) Protection Band (in feet) Error(infeet) Scan Line Speed:14.6feet/s Scan Line Speed: 8.6feet/s Scan Line Speed: 4.3feet/s (b) Mean Localization Error 0 0.2 0.4 0.6 0.8 1 1.5 2 2.5 3 3.5 4 Max Error(Line Scan at Different Speed) Protection Band (in feet) Error(infeet) Scan Line Speed: 14.6feet/s Scan Line Speed: 8.6feet/s Scan Line Speed: 4.3feet/s (c) Max Localization Error Figure 22. Impact of Protection Band and Scanning Speed 0 0.2 0.4 0.6 0.8 1 0 2 4 6 8 10 12 14 16 18 20 Unlocalized Node Number(Scan Line Speed 8.57feet/s) Protection Band (in feet) Numberofunlocalizednodeoutof46 Unlocalized node of Basic MSP Unlocalized node of Sequence Based MSP Unlocalized node of Iterative MSP (a) Number of Unlocalized Nodes 0 0.2 0.4 0.6 0.8 1 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 Mean Error(Scan Line Speed 8.57feet/s) Protection Band (in feet) Error(infeet) Mean Error of Basic MSP Mean Error of Sequence Based MSP Mean Error of Iterative MSP (b) Mean Localization Error 0 0.2 0.4 0.6 0.8 1 1.5 2 2.5 3 3.5 4 Max Error(Scan Line Speed 8.57feet/s) Protection Band (in feet) Error(infeet) Max Error of Basic MSP Max Error of Sequence Based MSP Max Error of Iterative MSP (c) Max Localization Error Figure 23. Impact of Protection Band under Different MSP Methods 3 4 5 6 7 8 9 10 11 0 0.5 1 1.5 2 2.5 Unlocalized Node Number(Protection Band: 0.35 feet) Anchor Number UnlocalizedNodeNumber 4 Scan Events at Speed 8.75feet/s 6 Scan Events at Speed 8.75feet/s 8 Scan Events at Speed 8.75feet/s (a) Number of Unlocalized Nodes 3 4 5 6 7 8 9 10 11 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Mean Error(Protection Band: 0.35 feet) Anchor Number Error(infeet) Mean Error of 4 Scan Events at Speed 8.75feet/s Mean Error of 6 Scan Events at Speed 8.75feet/s Mean Error of 8 Scan Events at Speed 8.75feet/s (b) Mean Localization Error 3 4 5 6 7 8 9 10 11 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 Max Error(Protection Band: 0.35 feet) Anchor Number Error(infeet) Max Error of 4 Scan Events at Speed 8.75feet/s Max Error of 6 Scan Events at Speed 8.75feet/s Max Error of 8 Scan Events at Speed 8.75feet/s (c) Max Localization Error Figure 24. Impact of the Number of Anchors and Scans 9.1.3 On Number of Anchors and Scans In this experiment, we show a tradeoff between hardware cost (anchors) with soft cost (events). Figure 24(a) shows that with more cutting lines created by anchors, the chance of unlocalized nodes increases slightly. We note that with a 0.35 feet protection band, the percentage of unlocalized nodes is very small, e.g., in the worst-case with 11 anchors, only 2 out of 46 nodes are not localized due to flipping. Figures 24(b) and 24(c) show the tradeoff between number of anchors and the number of scans. Obviously, with the number of anchors increases, the error drops significantly. With 11 anchors we can achieve a localization accuracy as low as 0.25 ∼ 0.35 feet, which is nearly a 60% improvement. Similarly, with increasing number of scans, the accuracy drops significantly as well. We can observe about 30% across all anchor settings when we increase the number of scans from 4 to 8. For example, with only 3 anchors, we can achieve 0.6-foot accuracy with 8 scans. 9.2 Outdoor System Evaluation The outdoor system evaluation contains two parts: (i) effective detection distance evaluation, which shows that the node sequence can be readily obtained, and (ii) sound propagation based localization, which shows the results of wavepropagation-based localization. 9.2.1 Effective Detection Distance Evaluation We firstly evaluate the sequence flip phenomenon in wave propagation. As shown in Figure 25, 20 motes were placed as five groups in front of the speaker, four nodes in each group at roughly the same distances to the speaker. The gap between each group is set to be 2, 3, 4 and 5 feet respectively in four experiments. Figure 26 shows the results. The x-axis in each subgraph indicates the group index. There are four nodes in each group (4 bars). The y-axis shows the detection rank (order) of each node in the node sequence. As distance between each group increases, number of flips in the resulting node sequence 27 Figure 25. Wave Detection 1 2 3 4 5 0 5 10 15 20 2 feet group distance Rank Group Index 1 2 3 4 5 0 5 10 15 20 3 feet group distance Rank Group Index 1 2 3 4 5 0 5 10 15 20 4 feet group distance Rank Group Index 1 2 3 4 5 0 5 10 15 20 5 feet group distance Rank Group Index Figure 26. Ranks vs. Distances 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 Y-Dimension(feet) X-Dimension (feet) Node 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 Y-Dimension(feet) X-Dimension (feet) Anchor Figure 27. Localization Error (Sound) decreases. For example, in the 2-foot distance subgraph, there are quite a few flips between nodes in adjacent and even nonadjacent groups, while in the 5-foot subgraph, flips between different groups disappeared in the test. 9.2.2 Sound Propagation Based Localization As shown in Figure 20, 20 motes are placed as a grid including 5 rows with 5 feet between each row and 4 columns with 4 feet between each column. Six 4KHz acoustic wave propagation events are generated around the mote grid by a speaker. Figure 27 shows the localization results using iterative MSP (3 times iterative processing) with a protection band of 3 feet. The average error of the localization results is 3 feet and the maximum error is 5 feet with one un-localized node. We found that sequence flip in wave propagation is more severe than that in the indoor, line-based test. This is expected due to the high propagation speed of sound. Currently we use MICAz mote, which is equipped with a low quality microphone. We believe that using a better speaker and more events, the system can yield better accuracy. Despite the hardware constrains, the MSP algorithm still successfully localized most of the nodes with good accuracy. 10 Conclusions In this paper, we present the first work that exploits the concept of node sequence processing to localize sensor nodes. We demonstrated that we could significantly improve localization accuracy by making full use of the information embedded in multiple easy-to-get one-dimensional node sequences. We proposed four novel optimization methods, exploiting order and marginal distribution among non-anchor nodes as well as the feedback information from early localization results. Importantly, these optimization methods can be used together, and improve accuracy additively. The practical issues of partial node sequence and sequence flip were identified and addressed in two physical system test-beds. We also evaluated performance at scale through analysis as well as extensive simulations. Results demonstrate that requiring neither costly hardware on sensor nodes nor precise event distribution, MSP can achieve a sub-foot accuracy with very few anchor nodes provided sufficient events. 11 References [1] CC2420 Data Sheet. Avaiable at http://www.chipcon.com/. [2] P. Bahl and V. N. Padmanabhan. Radar: An In-Building RF-Based User Location and Tracking System. In IEEE Infocom "00. [3] M. Broxton, J. Lifton, and J. Paradiso. Localizing A Sensor Network via Collaborative Processing of Global Stimuli. In EWSN "05. [4] N. Bulusu, J. Heidemann, and D. Estrin. GPS-Less Low Cost Outdoor Localization for Very Small Devices. IEEE Personal Communications Magazine, 7(4), 2000. [5] D. Culler, D. Estrin, and M. Srivastava. Overview of Sensor Networks. IEEE Computer Magazine, 2004. [6] J. Elson, L. Girod, and D. Estrin. Fine-Grained Network Time Synchronization Using Reference Broadcasts. In OSDI "02. [7] D. K. Goldenberg, P. Bihler, M. Gao, J. Fang, B. D. Anderson, A. Morse, and Y. Yang. Localization in Sparse Networks Using Sweeps. In MobiCom "06. [8] T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher. RangeFree Localization Schemes in Large-Scale Sensor Networks. In MobiCom "03. [9] B. Kusy, P. Dutta, P. Levis, M. Mar, A. Ledeczi, and D. Culler. Elapsed Time on Arrival: A Simple and Versatile Primitive for Canonical Time Synchronization Services. International Journal of ad-hoc and Ubiquitous Computing, 2(1), 2006. [10] L. Lazos and R. Poovendran. SeRLoc: Secure Range-Independent Localization for Wireless Sensor Networks. In WiSe "04. [11] M. Maroti, B. Kusy, G. Balogh, P. Volgyesi, A. Nadas, K. Molnar, S. Dora, and A. Ledeczi. Radio Interferometric Geolocation. In SenSys "05. [12] M. Maroti, B. Kusy, G. Simon, and A. Ledeczi. The Flooding Time Synchronization Protocol. In SenSys "04. [13] D. Moore, J. Leonard, D. Rus, and S. Teller. Robust Distributed Network Localization with Noise Range Measurements. In SenSys "04. [14] R. Nagpal and D. Coore. An Algorithm for Group Formation in an Amorphous Computer. In PDCS "98. [15] D. Niculescu and B. Nath. ad-hoc Positioning System. In GlobeCom "01. [16] D. Niculescu and B. Nath. ad-hoc Positioning System (APS) Using AOA. In InfoCom "03. [17] N. B. Priyantha, A. Chakraborty, and H. Balakrishnan. The Cricket Location-Support System. In MobiCom "00. [18] K. R¨omer. The Lighthouse Location System for Smart Dust. In MobiSys "03. [19] A. Savvides, C. C. Han, and M. B. Srivastava. Dynamic Fine-Grained Localization in ad-hoc Networks of Sensors. In MobiCom "01. [20] R. Stoleru, T. He, J. A. Stankovic, and D. Luebke. A High-Accuracy, Low-Cost Localization System for Wireless Sensor Networks. In SenSys "05. [21] R. Stoleru, P. Vicaire, T. He, and J. A. Stankovic. StarDust: a Flexible Architecture for Passive Localization in Wireless Sensor Networks. In SenSys "06. [22] E. W. Weisstein. Plane Division by Lines. mathworld.wolfram.com. [23] B. H. Wellenhoff, H. Lichtenegger, and J. Collins. Global Positions System: Theory and Practice,Fourth Edition. Springer Verlag, 1997. [24] K. Whitehouse. The Design of Calamari: an ad-hoc Localization System for Sensor Networks. In University of California at Berkeley, 2002. [25] Z. Zhong. MSP Evaluation and Implementation Report. Avaiable at http://www.cs.umn.edu/∼zhong/MSP. [26] G. Zhou, T. He, and J. A. Stankovic. Impact of Radio Irregularity on Wireless Sensor Networks. In MobiSys "04. 28

marginal distribution;node localization;multi-sequence positioning;listen-detect-assemble-report protocol;event distribution;range-based approach;spatiotemporal correlation;localization;node sequence process;distribution-based location estimation;massive uva-based deploment;wireless sensor network

train_C-45

StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks

"The problem of localization in wireless sensor networks where nodes do not use ranging hardware, re(...TRUNCATED)

"1 Introduction\nWireless Sensor Networks (WSN) have been envisioned\nto revolutionize the way human(...TRUNCATED)

"range;unique mapping;performance;image processing;connectivity;localization;scene labeling;probabil(...TRUNCATED)

train_C-46

TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs

"Archival storage of sensor data is necessary for applications that query, mine, and analyze such da(...TRUNCATED)

"1. Introduction\n1.1 Motivation\nMany different kinds of networked data-centric sensor\napplication(...TRUNCATED)

"analysis;archival storage;datum separation;sensor datum;index method;homogeneous architecture;flash(...TRUNCATED)

train_C-48

Multi-dimensional Range Queries in Sensor Networks∗

"In many sensor networks, data or events are named by attributes. Many of these attributes have scal(...TRUNCATED)

"1. INTRODUCTION\nIn wireless sensor networks, data or events will be named\nby attributes [15] or r(...TRUNCATED)

"datacentric storage system;multi-dimensional range query;multidimensional range query;event inserti(...TRUNCATED)

train_C-49

Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces

"Traditional mobile ad-hoc network (MANET) routing protocols assume that contemporaneous end-to-end (...TRUNCATED)

"1. INTRODUCTION\nMobile opportunistic networks are one kind of delay-tolerant\nnetwork (DTN) [6]. D(...TRUNCATED)

"contact trace;opportunistic network;route;epidemic protocol;frequent link break;end-to-end path;pro(...TRUNCATED)

train_C-50

CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses

"This paper describes the design, implementation and evaluation of a search and rescue system called(...TRUNCATED)

"1. INTRODUCTION\nSearch and rescue of people in emergency situation in a\ntimely manner is an extre(...TRUNCATED)

"intermittent network connectivity;gp receiver;pervasive computing;connected network;satellite trans(...TRUNCATED)

train_C-52

Fairness in Dead-Reckoning based Distributed Multi-Player Games

"In a distributed multi-player game that uses dead-reckoning vectors to exchange movement informatio(...TRUNCATED)

"1. INTRODUCTION\nIn a distributed multi-player game, players are normally\ndistributed across the I(...TRUNCATED)

"fairness;dead-reckoning vector;export error;network delay;budget based algorithm;clock synchronizat(...TRUNCATED)

train_C-53

Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games

"Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multi(...TRUNCATED)

"1. INTRODUCTION\nNowadays, many distributed multiplayer games adopt replicated\narchitectures. In s(...TRUNCATED)

"local lag;physical clock;time warp;usability and fairness;continuous replicate application;network (...TRUNCATED)